{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"colab":{"provenance":[{"file_id":"1RK3epfhx3DFNnSMm-omBXo1B2J5kRLJ1","timestamp":1741467874663}],"collapsed_sections":["6e8iIKBBlPnI","R7CkVt8UlilF"],"gpuType":"T4"},"accelerator":"GPU","kaggle":{"accelerator":"none","dataSources":[{"sourceId":84795,"databundleVersionId":11281725,"sourceType":"competition"},{"sourceId":203811899,"sourceType":"kernelVersion"},{"sourceId":226384747,"sourceType":"kernelVersion"},{"sourceId":226651204,"sourceType":"kernelVersion"},{"sourceId":236932,"sourceType":"modelInstanceVersion","modelInstanceId":202348,"modelId":224071},{"sourceId":256574,"sourceType":"modelInstanceVersion","modelInstanceId":204042,"modelId":225262}],"dockerImageVersionId":30919,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Master**","metadata":{"id":"cQTpyh6342ts"}},{"cell_type":"code","source":"# Load test data\n\nimport os\nimport zipfile\nfrom functools import cache\n# !mkdir -p /kaggle/tmp/konwinski-prize-alt\nos.makedirs(\"/kaggle/tmp/konwinski-prize-alt\", exist_ok=True)\n\n# !unzip -q -o /kaggle/input/konwinski-prize/data.a_zip -d /kaggle/tmp/konwinski-prize-alt/ 2>/dev/null || true\ntry:\n with zipfile.ZipFile(\"/kaggle/input/konwinski-prize/data.a_zip\", \"r\") as zip_ref:\n zip_ref.extractall(\"/kaggle/tmp/konwinski-prize-alt/\")\nexcept:\n pass","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:46:47.948717Z","iopub.execute_input":"2025-03-12T13:46:47.949013Z","iopub.status.idle":"2025-03-12T13:46:53.220276Z","shell.execute_reply.started":"2025-03-12T13:46:47.948992Z","shell.execute_reply":"2025-03-12T13:46:53.219599Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"REPO_PATH = \"repo\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:47:04.762234Z","iopub.execute_input":"2025-03-12T13:47:04.762556Z","iopub.status.idle":"2025-03-12T13:47:04.765517Z","shell.execute_reply.started":"2025-03-12T13:47:04.762532Z","shell.execute_reply":"2025-03-12T13:47:04.764809Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# @title Introduction\n# =========================\n\n#!/usr/bin/env python\n# coding: utf-8\n\"\"\"\nTarget: Final Integrated Notebook for KPrize Competition\n---------------------------------------------------------\nThis notebook integrates multiple components of the system, including:\n 1. Data Loading and Preprocessing:\n - Refines issue descriptions using classical normalization.\n - Uses open-source LLMs (T5-base) for rephrasing and summarization.\n - Computes a basic priority score.\n 2. Advanced Preprocessing:\n - Computes simulated sentiment analysis.\n - Detects duplicates using a global duplicate graph with embeddings.\n 3. Test Case Module:\n - Uses original test cases if available; otherwise, generates synthetic test cases via an LLM.\n 4. Adaptive Bucket & Cluster Discovery (ABCD) Framework:\n - Assigns issues to buckets using a hybrid approach (ML and heuristics).\n 5. File Relevance Extraction:\n - Retrieves relevant files from the repository using TF-IDF and a BM25-inspired alternative.\n 6. Patch Generation & Evaluation:\n - Uses a vLLM-based pipeline to select files, generate a git diff patch, verify its quality, and choose the best patch.\n 7. Training Lifecycle, Model Initialization, and Inference Server Setup.\n\nReplace placeholders with your actual implementations as needed.\n\"\"\"","metadata":{"id":"45a9d4a8-0e0d-48cf-8b18-b405e6b27146","cellView":"form","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:47:13.233541Z","iopub.execute_input":"2025-03-12T13:47:13.233828Z","iopub.status.idle":"2025-03-12T13:47:13.238947Z","shell.execute_reply.started":"2025-03-12T13:47:13.233806Z","shell.execute_reply":"2025-03-12T13:47:13.238367Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# @title Global Initialization\n# =========================\nimport sys, io, os, shutil, subprocess, time, json, logging, re, string, itertools, pickle, threading\nfrom typing import Optional, List, Tuple, Dict\nfrom functools import cache\n\nimport pandas as pd\nimport polars as pl\nimport random\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport networkx as nx\nimport numpy as np\nfrom enum import Enum\n\nimport kaggle_evaluation.konwinski_prize_inference_server\nimport unidiff\n\n# LLM Imports from Candidate\nfrom vllm import LLM, SamplingParams, RequestOutput\nimport warnings\n\nwarnings.simplefilter(\"ignore\")\n\n# Environment settings (from Candidate)\nos.environ[\"TRITON_PTXAS_PATH\"] = \"/usr/local/cuda/bin/ptxas\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1,2,3\"\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\n# Download necessary NLTK data\n# nltk.download('punkt', quiet=True)\n# nltk.download('stopwords', quiet=True)\n# nltk.download('wordnet', quiet=True)\n\n# Global Variables\ninstance_count = None\nfirst_prediction = True # Ensures predict() runs only once per session.\nMODE = \"inference\" # \"training\" or \"inference\"\nCONFIDENCE_THRESHOLD = 0.7 # Threshold for accepting a patch.\n# REPO_PATH = \"/path/to/repo\" # Repository path for file scanning.\nBUCKET_MODEL_CONFIDENCE_THRESHOLD = 0.5\nSUPERVISED_BUCKET_CONFIDENCE_THRESHOLD = 0.6\nREPO_CACHE: Dict[str, Tuple[List[str], List[str]]] = {} # Cache for repository file data.\nTRAINING_OUTPUT_FILE = \"training_model.pkl\"\n\n# Duplicate Analysis Structures\nduplicate_graph = nx.Graph()\nglobal_embeddings = {}\ngraph_lock = threading.Lock()\nstart_time = time.time()\nallowed_time = [start_time + 60 * 60]\n\n\n# LLM Pipeline Initialization for Basic Preprocessing\nmodel_32b = \"/kaggle/input/deepseek-r1/transformers/deepseek-r1-distill-qwen-32b-awq/1\"\n# model_32b = \"/kaggle/input/qwen2.5-coder/transformers/32b-instruct/1\"\nmodel_small = \"/kaggle/input/m/deepseek-ai/deepseek-r1/transformers/deepseek-r1-distill-qwen-1.5b/2\"\n # \"/kaggle/input/m/deepseek-ai/deepseek-r1/transformers/deepseek-r1-distill-qwen-1.5b/2\"\nmodel_3 = \"\"\nrunning_model = model_32b #That's what she said.\n\n\n# LLM Initialization for Patch Generation & Verification (Candidate style)\nif os.getenv(\"KAGGLE_KERNEL_RUN_TYPE\") or os.getenv(\"KAGGLE_IS_COMPETITION_RERUN\"):\n llm_model_pth: str = running_model\nelse:\n llm_model_pth: str = \"/root/volume/KirillR/QwQ-32B-Preview-AWQ\"\n\nBATCH_SIZE: int = 6\nVALIDATION_COPY_COUNT: int = 1\nMAX_TOKENS: int = 4096\nMAX_NUM_SEQS: int = 6\nMAX_MODEL_LEN: int = 32768\n\nllm: LLM = LLM(\n llm_model_pth,\n max_num_seqs=MAX_NUM_SEQS,\n max_model_len=MAX_MODEL_LEN,\n trust_remote_code=True,\n tensor_parallel_size=4,\n gpu_memory_utilization=0.95,\n seed=2024,\n)\ntokenizer = llm.get_tokenizer()\n\n\ndef count_tokens(text: str) -> int:\n return len(tokenizer.encode(text))","metadata":{"id":"yJLafJDWrNS4","executionInfo":{"status":"error","timestamp":1741473559510,"user_tz":420,"elapsed":10774,"user":{"displayName":"Joy G","userId":"16708522420429172728"}},"outputId":"8fc66f55-e365-4442-93dd-737a3dc6ab3e","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:50:35.306025Z","iopub.execute_input":"2025-03-12T13:50:35.306335Z","iopub.status.idle":"2025-03-12T13:54:25.43613Z","shell.execute_reply.started":"2025-03-12T13:50:35.306311Z","shell.execute_reply":"2025-03-12T13:54:25.435316Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# CHECK vLLM STATUS\n\ndef check_vllm():\n try:\n test_prompt = '''\n \n '''\n sampling_params = SamplingParams(temperature=0.7, max_tokens=10)\n\n # Run inference on a simple prompt\n output = llm.generate([test_prompt], sampling_params)\n \n # Check if the output is valid\n if output and isinstance(output[0], RequestOutput):\n print(\"✅ vLLM is properly loaded and ready!\")\n print(\"Sample Output:\", output[0].outputs[0].text)\n else:\n print(\"⚠️ vLLM is loaded, but the output is unexpected.\")\n except Exception as e:\n print(\"❌ vLLM failed to initialize:\", str(e))\n\n# Run the check\ncheck_vllm()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:54:25.437313Z","iopub.execute_input":"2025-03-12T13:54:25.437601Z","iopub.status.idle":"2025-03-12T13:54:25.873413Z","shell.execute_reply.started":"2025-03-12T13:54:25.437579Z","shell.execute_reply":"2025-03-12T13:54:25.872734Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"--------","metadata":{}},{"cell_type":"code","source":"# History tracking globals\nissue_number_count = 1\nhistory_dict = {}\n\nclass HistoryDict(Enum):\n # Define the keys\n CLEANED_ISSUE = \"cleaned_issue\"\n REFRAMED_ISSUE = \"reframed_issue\"\n SUMMARIZED_ISSUE = \"summarized_issue\"\n AST = \"ast\"\n TEST_CASES = \"test_cases\"\n PATCH_GENERATED = \"patch_generated\"\n # New fields for historical heuristics\n FILE_PATH = \"file_path\"\n FUNCTION_NAME = \"function_name\"\n ORIGINAL_CODE = \"original_code\"\n FIXED_CODE = \"fixed_code\"\n DIFF_PATCH = \"diff_patch\"\n MODIFIED_FUNCTIONS = \"modified_functions\"\n\ndef add_to_history(issue_key: str, field: HistoryDict, value: str) -> None:\n \"\"\"Add a value to the history dictionary for a specific issue and field.\n \n Args:\n issue_key: The unique identifier for the issue\n field: The HistoryDict enum value representing the field to update\n value: The value to store for this field\n \"\"\"\n global history_dict\n \n # Initialize issue entry if it doesn't exist\n if issue_key not in history_dict:\n history_dict[issue_key] = {}\n \n # Add/update the field value\n history_dict[issue_key][field.value] = value\n\ndef get_from_history(issue_key: str, field: HistoryDict) -> Optional[str]:\n \"\"\"Retrieve a value from the history dictionary for a specific issue and field.\n \n Args:\n issue_key: The unique identifier for the issue\n field: The HistoryDict enum value representing the field to retrieve\n \n Returns:\n The stored value if it exists, None otherwise\n \"\"\"\n return history_dict.get(issue_key, {}).get(field.value)\n\ndef get_issue_history(issue_key: str) -> Dict[str, str]:\n \"\"\"Get all stored history for a specific issue.\n \n Args:\n issue_key: The unique identifier for the issue\n \n Returns:\n Dictionary containing all stored fields and values for the issue\n \"\"\"\n return history_dict.get(issue_key, {})\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:71db7bac-d9d0-4c9a-a35b-c26eb582ee29.png)","metadata":{},"attachments":{"71db7bac-d9d0-4c9a-a35b-c26eb582ee29.png":{"image/png":"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"}}},{"cell_type":"code","source":"from typing import List, Dict, Set, Optional, Tuple, NamedTuple, Any","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class HistoryHeuristic(Enum):\n \"\"\"Enum for the seven HAFix historical heuristics.\"\"\"\n CFN_MODIFIED = \"CFN-modified\" # Co-evolved Functions' Names in the Modified Buggy File\n CFN_ALL = \"CFN-all\" # Co-evolved Functions' Names in All Modified Files\n FN_MODIFIED = \"FN-modified\" # Function Names in the Modified Buggy File\n FN_ALL = \"FN-all\" # Function Names in All Modified Files\n FLN_ALL = \"FLN-all\" # Co-evolved Files' Names\n FN_PAIR = \"FN-pair\" # Function Code Pairs\n FL_DIFF = \"FL-diff\" # File Diff Patch\n\ndef get_historical_heuristics(\n repo_archive: io.BytesIO,\n history_dict: Dict[str, Dict[str, Dict]],\n archive_path: str,\n file_path: str,\n function_name: str = None\n) -> Dict[str, Any]:\n \"\"\"Get all seven historical heuristics for a specific bug.\n\n Args:\n repo_archive: BytesIO object containing the repository archive\n history_dict: Dictionary containing historical data about issues/fixes\n archive_path: Path to the repository archive\n file_path: Path to the file containing the bug\n function_name: Optional name of the buggy function\n\n Returns:\n Dictionary mapping each HistoryHeuristic to its results:\n - CFN-modified: List of co-evolved function names in the buggy file\n - CFN-all: List of all co-evolved function names across files\n - FN-modified: List of all function names in the buggy file\n - FN-all: List of all function names in modified files\n - FLN-all: List of co-evolved file names\n - FN-pair: List of {before: str, after: str} dicts for function pairs\n - FL-diff: List of file diff patches\n \"\"\"\n # Initialize heuristics\n heuristics = {h.name: [] for h in HistoryHeuristic}\n\n # Extract repository contents\n repo_files = extract_repo(repo_archive)\n\n # Get all functions in the repository\n all_functions = {}\n for path, content in repo_files.items():\n try:\n all_functions[path] = extract_functions(content)\n except Exception:\n all_functions[path] = []\n\n # Find the buggy file\n buggy_file_content = None\n buggy_file_path = None\n for path, content in repo_files.items():\n if path.endswith(file_path):\n buggy_file_content = content\n buggy_file_path = path\n break\n\n if not buggy_file_content:\n return heuristics\n\n # FN-modified: All functions in the buggy file\n buggy_file_functions = all_functions.get(buggy_file_path, [])\n\n # FN-modified: All functions in the buggy file\n heuristics[HistoryHeuristic.FN_MODIFIED.name] = [\n f\"{func.get('parent_class', '')}.{func['name']}\" if func.get('parent_class') else func['name']\n for func in buggy_file_functions\n ]\n\n # FN-all: All functions in all files\n fn_all = []\n for path, funcs in all_functions.items():\n for func in funcs:\n if func.get('parent_class'):\n fn_all.append(f\"{func['parent_class']}.{func['name']}\")\n else:\n fn_all.append(func['name'])\n heuristics[HistoryHeuristic.FN_ALL.name] = list(set(fn_all))\n\n # Get \"blame\" information from history\n blame_info = get_blame_info_from_history(history_dict, archive_path, file_path)\n\n # Process historical information\n for info in blame_info:\n # CFN-modified: Co-evolved functions in the buggy file\n if 'modified_functions' in info:\n heuristics[HistoryHeuristic.CFN_MODIFIED.name].extend(info['modified_functions'])\n\n # CFN-all: Co-evolved functions in all modified files\n heuristics[HistoryHeuristic.CFN_ALL.name].extend(\n heuristics[HistoryHeuristic.CFN_MODIFIED.name]\n )\n\n # FLN-all: Co-evolved files\n heuristics[HistoryHeuristic.FLN_ALL.name].append(info['file_path'])\n\n # FN-pair: Function code before/after fix\n if function_name and info['function_name'] == function_name:\n if 'before_code' in info and 'after_code' in info:\n heuristics[HistoryHeuristic.FN_PAIR.name].append({\n 'before': info['before_code'],\n 'after': info['after_code']\n })\n\n # FL-diff: File diff patch\n if 'diff' in info:\n heuristics[HistoryHeuristic.FL_DIFF.name].append(info['diff'])\n\n # Remove duplicates\n for key in heuristics:\n if isinstance(heuristics[key], list) and key != HistoryHeuristic.FN_PAIR.name:\n heuristics[key] = list(set(heuristics[key]))\n\n return heuristics","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"implemented the HAFix historical heuristics approach:\n\n1. Uses seven historical heuristics to analyze code evolution:\n * CFN-modified: Co-evolved functions in modified files\n * CFN-all: All co-evolved functions\n * FN-modified: Functions in modified files\n * FN-all: All functions across files\n * FLN-all: Co-evolved file names\n * FN-pair: Function code pairs\n * FL-diff: File diff patches\n2. Integrates historical context into the bug-fixing process through:\n * Historical insights in context building\n * Previous fix patterns in patch generation\n * Co-evolution analysis for related changes\n3. Maintains existing functionality while enhancing it with historical insights","metadata":{}},{"cell_type":"code","source":"from dataclasses import dataclass\n# from typing import List, Dict, Set, Optional, Tuple, NamedTuple, Any\n\n# Configure logging\nlogging.basicConfig(\n level=logging.INFO,\n format='%(asctime)s - %(levelname)s - %(message)s'\n)\n\n# Global Constants\nMAX_TOKENS = 32768 # Maximum model context length\nENRICHED_CONTEXT_TOKENS = 2048 # Token limit for enriched context\nCHUNK_SIZE = 512 # Default chunk size for code blocks\n\nclass ContextSection(NamedTuple):\n \"\"\"Represents a section in the enriched context with priority-based ordering.\n \n Attributes:\n title: The section title/header\n content: The actual content of the section\n priority: Priority level (1-10, higher = more important)\n \n Methods:\n format: Returns formatted string representation of the section\n token_count: Calculates total tokens in the section\n \"\"\"\n title: str\n content: str\n priority: int # Higher number = higher priority\n \n def format(self) -> str:\n \"\"\"Returns the formatted section text.\"\"\"\n return f\"{self.title}:\\n{self.content}\"\n \n def token_count(self) -> int:\n \"\"\"Calculates the total tokens in the formatted section.\"\"\"\n return count_tokens(self.format())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:57:44.915755Z","iopub.execute_input":"2025-03-12T13:57:44.916092Z","iopub.status.idle":"2025-03-12T13:57:44.921519Z","shell.execute_reply.started":"2025-03-12T13:57:44.916067Z","shell.execute_reply":"2025-03-12T13:57:44.92087Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# extract key info\nimport re\n\ndef parse_issue_context(issue_text):\n extracted_info = {\n \"error_messages\": [],\n \"file_references\": [],\n \"code_snippets\": [],\n \"logs_stack_traces\": [],\n \"related_issues_prs\": [],\n \"functions\": [],\n \"variables\": []\n }\n\n # Error Messages:\n error_message_pattern = r\"[a-zA-Z]+Error:.*\" # Basic pattern, can be improved\n extracted_info[\"error_messages\"] = re.findall(error_message_pattern, issue_text)\n\n # File References:\n file_reference_pattern = r\"\\b[a-zA-Z0-9_-]+\\.(py|java|cpp|js|ts|c|h)\\b\"\n extracted_info[\"file_references\"] = re.findall(file_reference_pattern, issue_text)\n\n # Code Snippets:\n code_snippet_pattern = r\"(?:python|java|cpp|js|ts|c|h)?\\n(.*?)\\n\"\n extracted_info[\"code_snippets\"] = [match for match in re.findall(code_snippet_pattern, issue_text, re.DOTALL)]\n\n # Logs and Stack Traces:\n log_stack_trace_pattern = r\"line\\s+\\d+\\s+in\\s+[a-zA-Z0-9_-]+\\.(py|java|cpp|js|ts|c|h)\"\n extracted_info[\"logs_stack_traces\"] = re.findall(log_stack_trace_pattern, issue_text)\n\n # Related Issues or PRs:\n related_issues_prs_pattern = r\"(#|issue|pr|pull request)\\s*(\\d+)\"\n extracted_info[\"related_issues_prs\"] = re.findall(related_issues_prs_pattern, issue_text, re.IGNORECASE)\n\n # Functions (simple example, can be refined):\n function_pattern = r\"\\b[a-zA-Z0-9_]+\\(\"\n extracted_info[\"functions\"] = re.findall(function_pattern, issue_text)\n extracted_info[\"functions\"] = [func[:-1] for func in extracted_info[\"functions\"]] # Remove trailing '('\n\n # Variables (simple example, can be refined):\n variable_pattern = r\"\\b[a-zA-Z0-9_]+\\s*=\"\n extracted_info[\"variables\"] = re.findall(variable_pattern, issue_text)\n extracted_info[\"variables\"] = [var[:-2] for var in extracted_info[\"variables\"] if var[:-2] not in extracted_info[\"functions\"]] # Remove trailing '=' and already in functions\n\n return extracted_info\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:58:54.571294Z","iopub.execute_input":"2025-03-12T13:58:54.571626Z","iopub.status.idle":"2025-03-12T13:58:54.577727Z","shell.execute_reply.started":"2025-03-12T13:58:54.571602Z","shell.execute_reply":"2025-03-12T13:58:54.577082Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"2. AST","metadata":{}},{"cell_type":"code","source":"import ast\nimport os\n\ndef create_ast_for_file(file_path):\n try:\n with open(file_path, \"r\", encoding=\"utf-8\") as source_file:\n source_code = source_file.read()\n tree = ast.parse(source_code)\n return tree\n except Exception as e:\n print(f\"Error parsing {file_path}: {e}\")\n return None\n\ndef create_asts_for_repo(repo_dir):\n asts = {}\n for root, _, files in os.walk(repo_dir):\n for file in files:\n if file.endswith(\".py\"):\n file_path = os.path.join(root, file)\n ast_tree = create_ast_for_file(file_path)\n if ast_tree:\n asts[file_path] = ast_tree\n return asts\n\n# # usage\n# repo_dir = \"/content/data/repos/repo__astropy__astropy-16812\"\n# asts = create_asts_for_repo(repo_dir)\n# print(f\"Generated ASTs for {len(asts)} files.\")\n\n\n\ndef extract_ast_info(tree):\n extracted_info = {\n \"functions\": [],\n \"classes\": [],\n \"imports\": []\n }\n\n for node in ast.walk(tree):\n if isinstance(node, ast.FunctionDef):\n extracted_info[\"functions\"].append(node.name)\n elif isinstance(node, ast.ClassDef):\n extracted_info[\"classes\"].append(node.name)\n elif isinstance(node, ast.Import) or isinstance(node, ast.ImportFrom):\n for alias in node.names:\n extracted_info[\"imports\"].append(alias.name)\n\n return extracted_info\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:58:58.259619Z","iopub.execute_input":"2025-03-12T13:58:58.259919Z","iopub.status.idle":"2025-03-12T13:58:58.26639Z","shell.execute_reply.started":"2025-03-12T13:58:58.259897Z","shell.execute_reply":"2025-03-12T13:58:58.265775Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pickle\n\ndef save_asts_to_file(asts, output_file):\n with open(output_file, \"wb\") as f:\n pickle.dump(asts, f)\n\ndef load_asts_from_file(input_file):\n with open(input_file, \"rb\") as f:\n return pickle.load(f)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:59:02.202191Z","iopub.execute_input":"2025-03-12T13:59:02.202552Z","iopub.status.idle":"2025-03-12T13:59:02.206267Z","shell.execute_reply.started":"2025-03-12T13:59:02.202521Z","shell.execute_reply":"2025-03-12T13:59:02.205638Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Control Flow Graphs (CFGs), Data Dependence Graphs (DDGs), and Program Dependence Graphs (PDGs)","metadata":{}},{"cell_type":"markdown","source":"* Entry Point: analyzer.visit(tree)\n* For each node in AST:\n * If node is FunctionDef → calls visit_FunctionDef\n * If node is Assign → calls visit_Assign\n * If node is Name → calls visit_Name\n* self.generic_visit(node) # Processes child nodes","metadata":{}},{"cell_type":"code","source":"import ast\nimport os\nimport networkx as nx\nfrom typing import Dict, Any\nfrom dataclasses import dataclass\n\n@dataclass\nclass ProgramGraphs:\n ast: ast.AST\n cfg: nx.DiGraph\n ddg: nx.DiGraph\n pdg: nx.DiGraph\n metadata: Dict[str, Any]\n function_defs: List[ast.FunctionDef] \n var_defs: Dict[str, ast.Assign]\n\nclass EnhancedCodeAnalyzer(ast.NodeVisitor):\n\n def __init__(self):\n self.cfg = nx.DiGraph()\n self.ddg = nx.DiGraph()\n self.current_scope = []\n self.var_defs = {}\n self.node_stack = []\n self.current_file = \"\"\n self.function_defs = []\n\n # SWE-bench specific optimizations\n self.error_patterns = {\n 'AttributeError': self._handle_attribute_error,\n 'TypeError': self._handle_type_error,\n 'KeyError': self._handle_key_error\n }\n\n def visit(self, node):\n self.node_stack.append(node)\n result = super().visit(node) # Capture return value\n self.node_stack.pop()\n return result\n\n\n def visit_FunctionDef(self, node):\n # self.current_scope.append(node.name)\n \"\"\"Handles function definitions\"\"\"\n self.function_defs.append(node)\n self.current_scope.append(node.name)\n \n entry_node = f\"FUNC_ENTRY_{node.name}\"\n exit_node = f\"FUNC_EXIT_{node.name}\"\n\n self.cfg.add_node(entry_node)\n self.cfg.add_node(exit_node)\n self.cfg.add_edge(entry_node, node)\n\n self.generic_visit(node)\n\n self.cfg.add_edge(node, exit_node)\n self.current_scope.pop()\n\n def visit_Assign(self, node):\n # Track variable definitions\n for target in node.targets:\n if isinstance(target, ast.Name):\n var_name = target.id\n self.var_defs[var_name] = node\n self.ddg.add_node(node, type='def', var=var_name)\n\n self._connect_cfg_nodes(node)\n self.generic_visit(node)\n\n # def visit_Name(self, node):\n # if isinstance(node.ctx, ast.Load):\n # if node.id in self.var_defs:\n # def_node = self.var_defs[node.id]\n # self.ddg.add_edge(def_node, node)\n\n # self._connect_cfg_nodes(node)\n # self.generic_visit(node)\n def visit_Name(self, node):\n \"\"\"Enhanced variable tracking with store context handling\"\"\"\n \n # Track variable definitions (Store context)\n if isinstance(node.ctx, ast.Store):\n # Record where variables are assigned/created\n self.var_defs[node.id] = node\n self.ddg.add_node(node, type='def', var=node.id)\n # print(f\"Found variable assignment: {node.id} at line {node.lineno}\")\n \n # Track variable usage (Load context)\n elif isinstance(node.ctx, ast.Load):\n # Link usage to most recent definition\n if node.id in self.var_defs:\n def_node = self.var_defs[node.id]\n self.ddg.add_edge(def_node, node)\n # print(f\"Tracking data flow: {node.id} used at line {node.lineno}\"\n # f\" defined at line {def_node.lineno}\")\n \n # Handle deletions (Del context)\n elif isinstance(node.ctx, ast.Del):\n # Remove variable from tracking when deleted\n if node.id in self.var_defs:\n del self.var_defs[node.id]\n print(f\"Variable deleted: {node.id} at line {node.lineno}\")\n \n # Maintain CFG connections\n self._connect_cfg_nodes(node)\n self.generic_visit(node)\n\n\n def _connect_cfg_nodes(self, node):\n if self.node_stack:\n parent = self.node_stack[-1]\n self.cfg.add_edge(parent, node)\n\n def _handle_attribute_error(self, pattern):\n \"\"\"Special handling for common AttributeError patterns\"\"\"\n pass # Implementation for SWE-bench specific optimizations\n\n def _handle_type_error(self, pattern):\n \"\"\"Special handling for common TypeError patterns\"\"\"\n pass # Implementation for SWE-bench specific optimizations\n\n def _handle_key_error(self, pattern):\n \"\"\"Special handling for common KeyError patterns\"\"\"\n pass # Implementation for SWE-bench specific optimizations\n\n def build_pdg(self):\n \"\"\"Construct Program Dependence Graph with edge typing\"\"\"\n pdg = nx.compose(self.cfg, self.ddg)\n # nx.set_edge_attributes(pdg, 'control', name='edge_type')\n # nx.set_edge_attributes(pdg, 'data', name='edge_type',\n # edges=self.ddg.edges())\n\n # Set edge attributes based on NetworkX version\n if nx.__version__ >= '2.0':\n # Modern syntax (v2.x+)\n nx.set_edge_attributes(pdg, 'control', name='edge_type')\n nx.set_edge_attributes(pdg, {e: 'data' for e in self.ddg.edges()}, name='edge_type')\n else:\n # Legacy syntax (v1.x)\n nx.set_edge_attributes(pdg, 'edge_type', 'control')\n ddg_attrs = {e: {'edge_type': 'data'} for e in self.ddg.edges()}\n nx.set_edge_attributes(pdg, ddg_attrs)\n \n return pdg\n\ndef create_program_graphs(repo_dir: str) -> Dict[str, ProgramGraphs]:\n \"\"\"Create enhanced program graphs for entire repository\"\"\"\n graph_data = {}\n\n for root, _, files in os.walk(repo_dir):\n for file in files:\n \n if file.endswith(\".py\"):\n print(f\"looking at {file} ##################################################\")\n file_path = os.path.join(root, file)\n try:\n with open(file_path, \"r\", encoding=\"utf-8\") as f:\n source = f.read()\n\n tree = ast.parse(source)\n analyzer = EnhancedCodeAnalyzer()\n analyzer.current_file = file_path\n analyzer.visit(tree)\n\n pdg = analyzer.build_pdg()\n\n # Extract SWE-bench relevant metadata\n metadata = {\n 'num_functions': sum(1 for n in pdg.nodes\n if isinstance(n, ast.FunctionDef)),\n 'data_dependencies': len(analyzer.ddg.edges),\n 'control_flows': len(analyzer.cfg.edges)\n }\n print(f\"file metadata: {metadata} @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\")\n \n graph_data[file_path] = ProgramGraphs(\n ast=tree,\n cfg=analyzer.cfg,\n ddg=analyzer.ddg,\n pdg=pdg,\n metadata=metadata,\n\n function_defs=analyzer.function_defs,\n var_defs=analyzer.var_defs\n )\n\n except Exception as e:\n print(f\"Error processing {file_path}: {str(e)}\")\n\n return graph_data\n\ndef analyze_repo(repo_dir: str):\n \"\"\"Full analysis pipeline with SWE-bench optimizations\"\"\"\n graphs = create_program_graphs(repo_dir)\n print(\"@@@@@@@@@@@@@@@@analyze_repo@@@@@@@@@@@@@@@@@@\")\n print(graphs)\n print(\"@@@@@@@@@@@@@@@@analyze_repo@@@@@@@@@@@@@@@@@@\")\n # Example analysis: Find data-flow intensive files\n data_flow_rank = sorted(\n graphs.items(),\n key=lambda x: x[1].metadata['data_dependencies'],\n reverse=True\n )\n\n print(\"Most data-intensive files:\")\n for path, data in data_flow_rank[:3]:\n print(f\"{path}: {data.metadata['data_dependencies']} dependencies\")\n\n return graphs\n\nprint('analyze_repo')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:59:04.623161Z","iopub.execute_input":"2025-03-12T13:59:04.623455Z","iopub.status.idle":"2025-03-12T13:59:04.642807Z","shell.execute_reply.started":"2025-03-12T13:59:04.623434Z","shell.execute_reply":"2025-03-12T13:59:04.642137Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def extract_repo(repo_archive: io.BytesIO) -> Dict[str, str]:\n \"\"\"Extract Python files from the repository archive.\"\"\"\n file_contents = {}\n with zipfile.ZipFile(repo_archive) as zf:\n for file_info in zf.infolist():\n if file_info.filename.endswith('.py'):\n file_contents[file_info.filename] = zf.read(file_info.filename).decode('utf-8', errors='replace')\n return file_contents\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:59:40.162012Z","iopub.execute_input":"2025-03-12T13:59:40.162311Z","iopub.status.idle":"2025-03-12T13:59:40.166303Z","shell.execute_reply.started":"2025-03-12T13:59:40.162288Z","shell.execute_reply":"2025-03-12T13:59:40.165654Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def extract_functions(file_content: str) -> List[Dict[str, Any]]:\n \"\"\"Extract function information from a file using AST.\"\"\"\n try:\n tree = ast.parse(file_content)\n functions = []\n \n for node in ast.walk(tree):\n if isinstance(node, ast.FunctionDef) or isinstance(node, ast.AsyncFunctionDef):\n # Get source code lines\n lines = file_content.splitlines()\n end_lineno = getattr(node, 'end_lineno', None)\n \n if end_lineno is None:\n # For Python < 3.8 without end_lineno attribute\n source = lines[node.lineno-1]\n for i in range(node.lineno, len(lines)):\n line = lines[i]\n if not line.strip() or not line.startswith(' '):\n break\n source += '\\n' + line\n end_lineno = node.lineno + source.count('\\n')\n else:\n source = '\\n'.join(lines[node.lineno-1:end_lineno])\n \n # Create function info dictionary\n function_info = {\n 'name': node.name,\n 'lineno': node.lineno,\n 'end_lineno': end_lineno,\n 'source': source,\n 'args': [arg.arg for arg in node.args.args],\n }\n \n # Find parent class if this is a method\n parent_class = None\n for potential_parent in ast.walk(tree):\n if isinstance(potential_parent, ast.ClassDef):\n for child in ast.iter_child_nodes(potential_parent):\n if (isinstance(child, ast.FunctionDef) or isinstance(child, ast.AsyncFunctionDef)) and \\\n child.name == node.name and child.lineno == node.lineno:\n parent_class = potential_parent.name\n break\n if parent_class:\n break\n \n function_info['parent_class'] = parent_class\n functions.append(function_info)\n \n return functions\n \n except SyntaxError:\n return []","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:59:42.427315Z","iopub.execute_input":"2025-03-12T13:59:42.427653Z","iopub.status.idle":"2025-03-12T13:59:42.434721Z","shell.execute_reply.started":"2025-03-12T13:59:42.427628Z","shell.execute_reply":"2025-03-12T13:59:42.434069Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def create_file_diff(old_content: str, new_content: str, file_path: str = \"file.py\") -> str:\n \"\"\"Create a unified diff between two versions of a file.\"\"\"\n old_lines = old_content.splitlines()\n new_lines = new_content.splitlines()\n diff = difflib.unified_diff(\n old_lines, new_lines,\n fromfile=f'a/{file_path}',\n tofile=f'b/{file_path}',\n n=3\n )\n return '\\n'.join(diff)\n\n\ndef identify_modified_functions(old_functions: List[Dict], new_functions: List[Dict]) -> List[str]:\n \"\"\"Identify which functions were modified between two versions.\"\"\"\n modified_functions = []\n \n # Create dictionaries for quick lookup\n old_func_dict = {(f['name'], f.get('parent_class')): f for f in old_functions}\n new_func_dict = {(f['name'], f.get('parent_class')): f for f in new_functions}\n \n # Check for functions that exist in both but have different content\n for key, new_func in new_func_dict.items():\n if key in old_func_dict:\n old_func = old_func_dict[key]\n if new_func['source'] != old_func['source']:\n # Add function name, possibly with class prefix\n name, parent = key\n if parent:\n modified_functions.append(f\"{parent}.{name}\")\n else:\n modified_functions.append(name)\n \n return modified_functions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T13:59:57.673452Z","iopub.execute_input":"2025-03-12T13:59:57.673793Z","iopub.status.idle":"2025-03-12T13:59:57.679602Z","shell.execute_reply.started":"2025-03-12T13:59:57.673769Z","shell.execute_reply":"2025-03-12T13:59:57.678945Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def get_blame_info_from_history(\n history_dict: Dict[str, Dict[str, Dict]],\n archive_path: str,\n file_path: str\n) -> List[Dict[str, Any]]:\n \"\"\"Get \"blame\" information from the history dictionary.\"\"\"\n blame_info = []\n \n if archive_path not in history_dict:\n return blame_info\n \n for issue_key, issue_data in history_dict[archive_path].items():\n # Check if this issue involved the file we're interested in\n hist_file_path = issue_data.get(HistoryDict.FILE_PATH.value, '')\n \n if hist_file_path == file_path:\n info = {\n 'issue_key': issue_key,\n 'file_path': hist_file_path,\n 'function_name': issue_data.get(HistoryDict.FUNCTION_NAME.value, ''),\n 'modified_functions': issue_data.get(HistoryDict.MODIFIED_FUNCTIONS.value, []),\n 'diff': issue_data.get(HistoryDict.DIFF_PATCH.value, ''),\n 'before_code': issue_data.get(HistoryDict.ORIGINAL_CODE.value, ''),\n 'after_code': issue_data.get(HistoryDict.FIXED_CODE.value, '')\n }\n blame_info.append(info)\n \n return blame_info","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:00:01.055198Z","iopub.execute_input":"2025-03-12T14:00:01.055544Z","iopub.status.idle":"2025-03-12T14:00:01.060671Z","shell.execute_reply.started":"2025-03-12T14:00:01.055486Z","shell.execute_reply":"2025-03-12T14:00:01.060007Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def get_historical_heuristics(\n repo_archive: io.BytesIO,\n history_dict: Dict[str, Dict[str, Dict]],\n archive_path: str,\n file_path: str,\n function_name: str = None\n) -> Dict[str, Any]:\n \"\"\"Get all seven historical heuristics for a specific bug.\"\"\"\n # Initialize heuristics\n heuristics = {h.name: [] for h in HistoryHeuristic}\n \n # Extract repository contents\n repo_files = extract_repo(repo_archive)\n \n # Get all functions in the repository\n all_functions = {}\n for path, content in repo_files.items():\n try:\n all_functions[path] = extract_functions(content)\n except Exception:\n all_functions[path] = []\n \n # Find the buggy file\n buggy_file_content = None\n buggy_file_path = None\n for path, content in repo_files.items():\n if path.endswith(file_path):\n buggy_file_content = content\n buggy_file_path = path\n break\n \n if not buggy_file_content:\n return heuristics\n \n # FN-modified: All functions in the buggy file\n buggy_file_functions = all_functions.get(buggy_file_path, [])\n \n # FN-modified: All functions in the buggy file\n heuristics[HistoryHeuristic.FN_MODIFIED.name] = [\n f\"{func.get('parent_class', '')}.{func['name']}\" if func.get('parent_class') else func['name']\n for func in buggy_file_functions\n ]\n \n # FN-all: All functions in all files\n fn_all = []\n for path, funcs in all_functions.items():\n for func in funcs:\n if func.get('parent_class'):\n fn_all.append(f\"{func['parent_class']}.{func['name']}\")\n else:\n fn_all.append(func['name'])\n heuristics[HistoryHeuristic.FN_ALL.name] = list(set(fn_all))\n \n # Get \"blame\" information from history\n blame_info = get_blame_info_from_history(history_dict, archive_path, file_path)\n \n # Process historical information\n for info in blame_info:\n # CFN-modified: Co-evolved functions in the buggy file\n if 'modified_functions' in info:\n heuristics[HistoryHeuristic.CFN_MODIFIED.name].extend(info['modified_functions'])\n \n # CFN-all: Co-evolved functions in all modified files\n heuristics[HistoryHeuristic.CFN_ALL.name].extend(\n heuristics[HistoryHeuristic.CFN_MODIFIED.name]\n )\n \n # FLN-all: Co-evolved files\n heuristics[HistoryHeuristic.FLN_ALL.name].append(info['file_path'])\n \n # FN-pair: Function code before/after fix\n if function_name and info['function_name'] == function_name:\n if 'before_code' in info and 'after_code' in info:\n heuristics[HistoryHeuristic.FN_PAIR.name].append({\n 'before': info['before_code'],\n 'after': info['after_code']\n })\n \n # FL-diff: File diff patch\n if 'diff' in info:\n heuristics[HistoryHeuristic.FL_DIFF.name].append(info['diff'])\n \n # Remove duplicates\n for key in heuristics:\n if isinstance(heuristics[key], list) and key != HistoryHeuristic.FN_PAIR.name:\n heuristics[key] = list(set(heuristics[key]))\n \n return heuristics","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:00:04.10771Z","iopub.execute_input":"2025-03-12T14:00:04.108008Z","iopub.status.idle":"2025-03-12T14:00:04.116923Z","shell.execute_reply.started":"2025-03-12T14:00:04.107985Z","shell.execute_reply":"2025-03-12T14:00:04.116276Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"issue_number_count = 1\nhistory_dict = {}\n\nfrom enum import Enum\nclass HistoryDict(Enum):\n # Define the keys\n CLEANED_ISSUE = \"cleaned_issue\"\n REFRAMED_ISSUE = \"reframed_issue\"\n SUMMARIZED_ISSUE = \"summarized_issue\"\n AST = \"ast\"\n TEST_CASES = \"test_cases\"\n PATCH_GENERATED = \"patch_generated\"\n # New fields for historical heuristics\n FILE_PATH = \"file_path\"\n FUNCTION_NAME = \"function_name\"\n ORIGINAL_CODE = \"original_code\"\n FIXED_CODE = \"fixed_code\"\n DIFF_PATCH = \"diff_patch\"\n MODIFIED_FUNCTIONS = \"modified_functions\"\n\nprint('HistoryDict')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:02:25.148588Z","iopub.execute_input":"2025-03-12T14:02:25.148902Z","iopub.status.idle":"2025-03-12T14:02:25.153697Z","shell.execute_reply.started":"2025-03-12T14:02:25.14888Z","shell.execute_reply":"2025-03-12T14:02:25.153028Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def add_history_dict(field : HistoryDict, repo_archive : str, issue_key : str, value:str):\n \"\"\"\n adding the history generated data from other functions to the dictionary\n {repo: {issue_key:{HistoryDict:data}}}\n \"\"\"\n\n # init the repo key\n if repo_archive not in history_dict:\n history_dict[repo_archive] = {}\n\n # put data in the history_dict\n if issue_key not in history_dict[repo_archive]:\n history_dict[repo_archive][issue_key] = {\n field.value: value\n }\n else:\n history_dict[repo_archive][issue_key][field.value] = value\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:02:27.291755Z","iopub.execute_input":"2025-03-12T14:02:27.292047Z","iopub.status.idle":"2025-03-12T14:02:27.296314Z","shell.execute_reply.started":"2025-03-12T14:02:27.292025Z","shell.execute_reply":"2025-03-12T14:02:27.295664Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def add_historical_data(\n # history_dict: Dict[str, Dict[str, Dict]],\n issue_key: str,\n archive_path: str,\n file_path: str,\n buggy_function_name: str,\n fixed_code: str,\n original_code: str\n):\n \"\"\"Add historical data to the history dictionary.\"\"\"\n # Get or create repository entry\n if archive_path not in history_dict:\n history_dict[archive_path] = {}\n \n # Get or create issue entry\n if issue_key not in history_dict[archive_path]:\n history_dict[archive_path][issue_key] = {}\n \n issue_data = history_dict[archive_path][issue_key]\n \n # Store file and function information\n issue_data[HistoryDict.FILE_PATH.value] = file_path\n issue_data[HistoryDict.FUNCTION_NAME.value] = buggy_function_name\n \n # Store before/after code\n issue_data[HistoryDict.ORIGINAL_CODE.value] = original_code\n issue_data[HistoryDict.FIXED_CODE.value] = fixed_code\n \n # Create diff\n issue_data[HistoryDict.DIFF_PATCH.value] = create_file_diff(original_code, fixed_code, file_path)\n \n # Extract functions from before and after\n try:\n before_functions = extract_functions(original_code)\n after_functions = extract_functions(fixed_code)\n \n # Identify modified functions\n modified_functions = identify_modified_functions(before_functions, after_functions)\n issue_data[HistoryDict.MODIFIED_FUNCTIONS.value] = modified_functions\n \n except Exception as e:\n issue_data[\"parsing_error\"] = str(e)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:02:30.13403Z","iopub.execute_input":"2025-03-12T14:02:30.134347Z","iopub.status.idle":"2025-03-12T14:02:30.139818Z","shell.execute_reply.started":"2025-03-12T14:02:30.134323Z","shell.execute_reply":"2025-03-12T14:02:30.139123Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def select_relevant_files(problem_statement: str, graph_data: Dict[str, Any]) -> List[str]:\n \"\"\"\n Use LLM to analyze the problem statement and graph data to identify relevant files.\n \n Args:\n problem_statement (str): The problem statement describing the issue.\n graph_data (Dict[str, Any]): Serialized graph data for the codebase.\n \n Returns:\n List[str]: List of file paths that are relevant to the problem statement.\n \"\"\"\n # Construct the prompt for the LLM\n prompt = f\"Analyze the following problem statement and graph data to identify relevant files:\\n\\n\"\n prompt += f\"Problem Statement: {problem_statement}\\n\\n\"\n prompt += f\"Graph Data: {json.dumps(graph_data)}\\n\\n\"\n prompt += \"List the file paths that are relevant to the problem statement.\"\n\n # Use the LLM to generate a response\n sampling_params = SamplingParams(temperature=0.6, min_p=0.01, skip_special_tokens=True, max_tokens=MAX_TOKENS)\n request_outputs: List[RequestOutput] = llm.generate([prompt], sampling_params=sampling_params)\n\n # Extract the list of file paths from the LLM response\n if request_outputs:\n response_text = request_outputs[0].outputs[0].text.strip()\n relevant_files = response_text.splitlines()\n return relevant_files\n else:\n return []","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:03:00.86969Z","iopub.execute_input":"2025-03-12T14:03:00.869986Z","iopub.status.idle":"2025-03-12T14:03:00.874621Z","shell.execute_reply.started":"2025-03-12T14:03:00.869964Z","shell.execute_reply":"2025-03-12T14:03:00.873957Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"------","metadata":{}},{"cell_type":"code","source":"# @title Duplicate Analysis & Preprocessing\n# =========================\ndef compute_embedding(issue: dict) -> np.ndarray:\n seed = hash(issue[\"description\"]) % 123456\n rng = np.random.default_rng(seed)\n return rng.random((1, 100))\n\n\ndef add_issue_to_duplicate_graph(issue: dict, similarity_threshold: float = 0.8) -> int:\n issue_id = issue.get(\"issue_id\")\n if not issue_id:\n issue_id = str(hash(issue[\"description\"]))\n issue[\"issue_id\"] = issue_id\n embedding = compute_embedding(issue)\n with graph_lock:\n if issue_id not in duplicate_graph:\n duplicate_graph.add_node(issue_id)\n global_embeddings[issue_id] = embedding\n for existing_id, existing_embedding in global_embeddings.items():\n if existing_id == issue_id:\n continue\n sim = cosine_similarity(embedding, existing_embedding)[0][0]\n if sim >= similarity_threshold:\n duplicate_graph.add_edge(issue_id, existing_id)\n comp = nx.node_connected_component(duplicate_graph, issue_id)\n comp_size = len(comp)\n return comp_size\n\n\ndef duplicate_issue_analysis(issue: dict) -> bool:\n return add_issue_to_duplicate_graph(issue) > 1","metadata":{"cellView":"form","id":"a3I5ZE5Y3aL0","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:03:03.405687Z","iopub.execute_input":"2025-03-12T14:03:03.406007Z","iopub.status.idle":"2025-03-12T14:03:03.411786Z","shell.execute_reply.started":"2025-03-12T14:03:03.405983Z","shell.execute_reply":"2025-03-12T14:03:03.411116Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# @title Rephrase & Summarize\n# =========================\n\ndef refine_issue_description(text: str) -> str:\n text = text.lower()\n text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n text = re.sub(r'\\s+', ' ', text).strip()\n tokens = nltk.word_tokenize(text)\n stop_words = set(stopwords.words('english'))\n filtered_tokens = [word for word in tokens if word not in stop_words]\n lemmatizer = WordNetLemmatizer()\n lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]\n return ' '.join(lemmatized_tokens)\n\n\ndef build_basic_rephrase_prompt(new_issue: str) -> str:\n prompt = (\n \"### Task: Rephrase the following GitHub issue to improve clarity, conciseness, and actionability. Think carefully.\\n\\n\"\n \"### Instructions:\\n\"\n \"- **Clearly state the problem**, including any errors, unexpected behaviors, or missing features.\\n\"\n \"- **Provide relevant context** (affected components, functions, or code sections).\\n\"\n \"- **Mention expected behavior**, if applicable.\\n\"\n \"- **If the issue suggests a fix or contains useful hints, retain them succinctly**.\\n\"\n \"- **Remove unnecessary details**, redundant logs, and lengthy code snippets (unless essential for understanding).\\n\"\n \"- **Ensure the summary is suitable for efficient triage and downstream AI processing**.\\n\\n\"\n f\"### Original Issue Description:\\n[{new_issue}]\\n\\n\"\n \"### Rephrased Issue: \\nOutput: Rephrased:\"\n )\n\n return prompt\n\n\ndef rephrase_issue_description(prompt_text: str) -> str:\n sampling_params = SamplingParams(\n temperature=0.6,\n min_p=0.01,\n skip_special_tokens=True,\n max_tokens=MAX_TOKENS\n )\n\n request_outputs: List[RequestOutput] = llm.generate([prompt_text], sampling_params=sampling_params)\n\n if not request_outputs:\n return \"\"\n\n # Extract generated text\n raw_output = request_outputs[0].outputs[0].text.strip()\n\n summary_marker = \"Output: Rephrased:\"\n rephrased = raw_output.split(summary_marker)[-1].strip() if summary_marker in raw_output else raw_output\n\n return rephrased\n\ndef generate_rephrased(problem_statement):\n # Build prompt with problem statement\n\n prompt = build_basic_rephrase_prompt(problem_statement)\n\n print(\"------------- PROMPT ---------------\")\n print(prompt, \"\\n\\n\")\n\n # Generate rephrased issue\n summary = rephrase_issue_description(prompt)\n print(\"\\n----------- REPHRASED OUTPUT -----------\")\n print(summary)\n\n return summary\n\ndef build_basic_summary_prompt(new_issue: str) -> str:\n prompt = (\n \"Task: Summarize the following GitHub issue clearly to guide a resolution.\\n\\n\"\n \"Instructions:\\n\"\n \"1. Clearly identify the specific error, problem, or functionality requested.\\n\"\n \"2. Mention the context (affected functions, methods, or components).\\n\"\n \"3. If the solution or fix is explicitly stated or obvious, include it succinctly.\\n\"\n \"4. Avoid repeating unnecessary code snippets, error traces, or verbose logs.\\n\"\n \"5) Identify and summarize the current problem issue and expected behaviour.\\n\"\n \"6. Refine your summary to be suitable for quick triage and efficient downstream LLM prompting.\\n\\n\"\n f\"Issue Description:\\n[{new_issue}]\\n\\n\"\n \"Concise Summary:\"\n )\n\n return prompt\n\n\ndef summarize_issue_description(prompt_text: str) -> str:\n sampling_params = SamplingParams(\n temperature=0.6,\n min_p=0.01,\n skip_special_tokens=True,\n max_tokens=MAX_TOKENS\n )\n\n # Use vLLM generate method with raw prompt (without applying chat template)\n request_outputs: List[RequestOutput] = llm.generate([prompt_text], sampling_params=sampling_params)\n\n if not request_outputs:\n return \"\"\n\n # Extract generated text\n raw_output = request_outputs[0].outputs[0].text.strip()\n\n # Post-processing to extract summary\n summary_marker = \"Output: Summary:\"\n summary = raw_output.split(summary_marker)[-1].strip() if summary_marker in raw_output else raw_output\n\n return summary\n\n\ndef generate_summary(problem_statement, basic_prompt = True, few_shot_examples = None):\n # Build prompt with problem statement\n if basic_prompt:\n prompt = build_basic_summary_prompt(problem_statement)\n else:\n prompt = build_few_shot_prompt(few_shot_examples, problem_statement)\n\n print(\"------------- PROMPT ---------------\")\n print(prompt, \"\\n\\n\")\n\n # Generate summary\n summary = summarize_issue_description(prompt)\n print(\"\\n----------- SUMMARY OUTPUT -----------\")\n print(summary)\n\n\ndef basic_preprocessing(issue: dict) -> dict:\n logging.info(\"Performing basic preprocessing...\")\n issue[\"description_processed\"] = refine_issue_description(issue[\"description\"])\n issue[\"description_rephrased\"] = rephrase_issue_description(issue[\"description\"])\n issue[\"description_summarized\"] = summarize_issue_description(issue[\"description\"])\n # issue[\"priority\"] = random.uniform(0, 1)\n return issue\n\n\ndef advanced_preprocessing(issue: dict) -> dict:\n logging.info(\"Performing advanced preprocessing...\")\n # issue[\"sentiment\"] = random.uniform(0, 1)\n # issue[\"duplicate\"] = duplicate_issue_analysis(issue)\n return issue","metadata":{"id":"yZ_x4KfvrRhF","cellView":"form","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:03:06.534155Z","iopub.execute_input":"2025-03-12T14:03:06.534478Z","iopub.status.idle":"2025-03-12T14:03:06.545453Z","shell.execute_reply.started":"2025-03-12T14:03:06.534451Z","shell.execute_reply":"2025-03-12T14:03:06.544804Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# @title Retrieve Relevant Files\n# =========================\ndef stringify_directory(directory: str) -> str:\n full_paths: List[str] = []\n banned_strings = [\".venv\", \".pyc\", \".txt\", \".pytest_cache\", \".github\", \"/doc/\"]\n\n for root, dirs, files in os.walk(directory):\n for file in files:\n for banned_string in banned_strings:\n if banned_string in root or banned_string in file:\n break\n else:\n full_path: str = os.path.join(root, file)\n full_paths.append(full_path)\n print(\"==================full_paths\",full_paths)\n return \"\\n\".join(full_paths)\n\n\nimport re\n\n\ndef extract_file_query(xml_content):\n import xml.etree.ElementTree as ET\n\n # Prepare a data structure to collect results\n parsed_data = {}\n pattern = r'(.*?)'\n matches = re.findall(pattern, xml_content, re.DOTALL)\n \n for match in matches:\n try:\n # Parse the XML\n root = ET.fromstring(\"\" + match + \"\")\n \n # Find all elements\n for entry in root.findall(\"entry\"):\n # Extract the text\n filepath = entry.find(\"filepath\")\n filepath_text = filepath.text.strip() if filepath is not None else None\n \n # Locate container\n strings_container = entry.find(\"strings_to_search\")\n \n # Gather each text\n search_strings = []\n if strings_container is not None:\n for s in strings_container.findall(\"string_to_search\"):\n if s.text is not None:\n search_strings.append(s.text.strip())\n \n # Store in a dictionary: { filepath: [search_strings...] }\n parsed_data[filepath_text] = search_strings\n except:\n print(\"Error parsing output\", xml_content)\n return {}\n \n return parsed_data\n\nreading_prompt = \"\"\"\nYou will be implementing a git diff patch to solve an issue with the code repository.\nYou will first need to select files in the file directory.\n\nThis is the problem statement.\n\n{problem_statement}\n\nThis is the file directory\n\n\n{directory_string}\n\n\nWhich files should be inspected so that we can solve the problem?\nWhen we inspect each file, what strings should be searched?\n\nIMPORTANT: Return the strings to search in this format\n\n(explanation)\n\n\n \n filepath \n \n string_to_search\n ...\n string_to_search\n \n \n\n\n(explanation)\n\n\n \n filepath\n \n string_to_search\n ...\n string_to_search\n \n \n\n...\n\nNotes:\n- Make sure to encode each entry between and \n- Return the FULL filepath - exactly as specified in and \n - Example: repo/path/to/directory/file.py\n- If you are searching for a word instead of a substring, maybe add spaces or brackets before and after the string\n For example, if you are searching for uses of the function `calculate`, use ` calculate(` as the search string instead of `calculate`\n- Prefer searching longer strings\n- Only inspect the necessary files\n\"\"\".strip()","metadata":{"id":"7q5_xlQNrYdu","cellView":"form","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:03:11.489375Z","iopub.execute_input":"2025-03-12T14:03:11.489717Z","iopub.status.idle":"2025-03-12T14:03:11.501222Z","shell.execute_reply.started":"2025-03-12T14:03:11.489693Z","shell.execute_reply":"2025-03-12T14:03:11.500509Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def get_selection_query(\n directory_string: str, \n problem_statement: str,\n relevant_files: Optional[List[str]] = None,\n graph_insights: Optional[List[Dict]] = None\n) -> Tuple[List[str], List[Dict[str, List[str]]]]:\n # Add program graph insights to the prompt\n graph_context = \"\"\n if graph_insights:\n graph_context = \"\\nProgram graph analysis insights:\\n\"\n for insight in graph_insights:\n graph_context += f\"\\nFile: {insight['file']}\\n\"\n graph_context += f\"Functions: {', '.join(insight['functions'])}\\n\"\n graph_context += f\"Data Dependencies: {insight['data_deps']}\\n\"\n graph_context += f\"Control Flows: {insight['control_flows']}\\n\"\n graph_context += \"\\nConsider these insights when selecting relevant files.\\n\"\n elif relevant_files:\n graph_context = \"\\nProgram graph analysis suggests these files are relevant:\\n\"\n graph_context += \"\\n\".join(relevant_files)\n graph_context += \"\\nConsider these files first, but also check others if needed.\\n\"\n \n sampling_params = SamplingParams(\n temperature=0.2,\n min_p=0.01,\n skip_special_tokens=True,\n max_tokens=MAX_TOKENS\n )\n\n list_of_messages: List[List[Dict[str, str]]] = [\n [\n {\n \"role\": \"system\",\n \"content\": \"You must respond with ONLY the XML format specified by the user. Do not explain your reasoning. Do not include any other text.\"\n },\n {\n \"role\": \"user\",\n \"content\": reading_prompt.format(\n problem_statement=problem_statement,\n directory_string=directory_string\n ) + graph_context\n }\n ] for _ in range(BATCH_SIZE)\n ]\n prompt_texts: List[str] = [\n tokenizer.apply_chat_template(conversation=messages, tokenize=False, add_generation_prompt=True) + \"\\n\"\n for messages in list_of_messages\n ]\n # print(\"===================prompt_texts\",prompt_texts)\n print(\"get_selection_query\", [count_tokens(text) for text in prompt_texts])\n request_outputs: List[RequestOutput] = llm.generate(prompt_texts, sampling_params=sampling_params)\n # print(\"========================= request_outputs = \", request_outputs)\n if not request_outputs:\n return [], []\n\n response_texts: List[str] = [ro.outputs[0].text for ro in request_outputs]\n # print(\"===================response_texts\",response_texts)\n\n print(\"get_selection_query\", [count_tokens(text) for text in response_texts])\n completion_texts = [pt + rt for pt, rt in zip(prompt_texts, response_texts)]\n # print(response_texts)\n\n file_queries = [extract_file_query(rt) for rt in response_texts]\n print(\"This is debug_code : \",file_queries)\n # print(\"================ completion_texts = \", completion_texts)\n # print(\"================ file_queries = \", file_queries)\n\n return completion_texts, file_queries\n\n\n# def get_selection_query(directory_string: str, problem_statement: str) -> Tuple[List[str], List[Dict[str, List[str]]]]:\n# sampling_params = SamplingParams(\n# temperature=0.2, \n# min_p=0.01, \n# skip_special_tokens=True, \n# max_tokens=MAX_TOKENS\n# )\n \n# list_of_messages: List[List[Dict[str, str]]] = [\n# [\n# {\n# \"role\": \"system\", \n# \"content\": \"You must respond with ONLY the XML format specified by the user. Do not explain your reasoning. Do not include any other text.\"\n# },\n# {\n# \"role\": \"user\", \n# \"content\": reading_prompt.format(\n# problem_statement=problem_statement,\n# directory_string=directory_string\n# )\n# }\n# ] for _ in range(BATCH_SIZE)\n# ]\n# prompt_texts: List[str] = [\n# tokenizer.apply_chat_template(conversation=messages, tokenize=False, add_generation_prompt=True) + \"\\n\"\n# for messages in list_of_messages\n# ]\n# # print(\"===================prompt_texts\",prompt_texts)\n# print(\"get_selection_query\", [count_tokens(text) for text in prompt_texts])\n# request_outputs: List[RequestOutput] = llm.generate(prompt_texts, sampling_params=sampling_params)\n# # print(\"========================= request_outputs = \", request_outputs)\n# if not request_outputs:\n# return [], []\n\n# response_texts: List[str] = [ro.outputs[0].text for ro in request_outputs]\n# # print(\"===================response_texts\",response_texts)\n\n# print(\"get_selection_query\", [count_tokens(text) for text in response_texts])\n# completion_texts = [pt + rt for pt, rt in zip(prompt_texts, response_texts)]\n# # print(response_texts)\n \n# file_queries = [extract_file_query(rt) for rt in response_texts]\n# print(\"This is debug_code : \",file_queries)\n# # print(\"================ completion_texts = \", completion_texts)\n# # print(\"================ file_queries = \", file_queries)\n \n# return completion_texts, file_queries","metadata":{"id":"7q5_xlQNrYdu","cellView":"form","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:03:11.489375Z","iopub.execute_input":"2025-03-12T14:03:11.489717Z","iopub.status.idle":"2025-03-12T14:03:11.501222Z","shell.execute_reply.started":"2025-03-12T14:03:11.489693Z","shell.execute_reply":"2025-03-12T14:03:11.500509Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from dataclasses import dataclass\nfrom typing import List, Dict, Set, Optional, Tuple\nimport ast\nimport re\n\n\n@dataclass\nclass CodeBlock:\n \"\"\"Represents a logical block of code with metadata\"\"\"\n content: str\n block_type: str # 'import', 'function', 'class', 'error_handler', etc.\n dependencies: Set[str] # imported modules, called functions\n relevance_score: float\n token_count: int\n start_line: int\n end_line: int\n \nclass AdaptiveChunker:\n def __init__(self, max_tokens: int = 512):\n self.max_tokens = max_tokens\n self.ast_parser = ast.parse\n \n def _extract_dependencies(self, node: ast.AST) -> Set[str]:\n \"\"\"Extract module and function dependencies from an AST node\"\"\"\n dependencies = set()\n \n for child in ast.walk(node):\n if isinstance(child, ast.Import):\n for name in child.names:\n dependencies.add(name.name)\n elif isinstance(child, ast.ImportFrom):\n dependencies.add(child.module)\n elif isinstance(child, ast.Call):\n if isinstance(child.func, ast.Name):\n dependencies.add(child.func.id)\n elif isinstance(child.func, ast.Attribute):\n dependencies.add(child.func.attr)\n \n return dependencies\n \n def _calculate_relevance(self, block: CodeBlock, search_terms: List[str]) -> float:\n \"\"\"Calculate block relevance score based on multiple factors\"\"\"\n score = 0.0\n \n # Term density\n term_matches = sum(term.lower() in block.content.lower() for term in search_terms)\n score += term_matches * 0.3\n \n # Critical patterns\n if 'error' in block.block_type.lower() or 'exception' in block.content.lower():\n score += 0.2\n if 'api' in block.block_type.lower() or any(word in block.content.lower() for word in ['endpoint', 'route', 'handler']):\n score += 0.2\n \n # Dependency relevance\n if any(term.lower() in dep.lower() for term in search_terms for dep in block.dependencies):\n score += 0.3\n \n # Structural importance\n if block.block_type in ['class', 'function']:\n score += 0.1\n \n return min(score, 1.0)\n\n def parse_code_blocks(self, code: str, search_terms: List[str]) -> List[CodeBlock]:\n \"\"\"Parse code into logical blocks with metadata\"\"\"\n blocks = []\n try:\n tree = self.ast_parser(code)\n \n # Parse code into blocks\n lines = code.split('\\n')\n \n for node in ast.walk(tree):\n if isinstance(node, (ast.FunctionDef, ast.ClassDef, ast.AsyncFunctionDef)):\n start_line = node.lineno\n end_line = node.end_lineno if hasattr(node, 'end_lineno') else start_line\n \n # Get block content\n block_lines = lines[start_line-1:end_line]\n content = '\\n'.join(block_lines)\n \n # Create block with metadata\n block = CodeBlock(\n content=content,\n block_type=node.__class__.__name__,\n dependencies=self._extract_dependencies(node),\n relevance_score=0.0, # Will be calculated later\n token_count=count_tokens(content),\n start_line=start_line,\n end_line=end_line\n )\n \n # Calculate relevance score\n block.relevance_score = self._calculate_relevance(block, search_terms)\n \n blocks.append(block)\n \n # Handle imports separately\n imports_block = []\n for node in ast.walk(tree):\n if isinstance(node, (ast.Import, ast.ImportFrom)):\n start_line = node.lineno\n imports_block.extend(lines[start_line-1:start_line])\n \n if imports_block:\n content = '\\n'.join(imports_block)\n blocks.append(CodeBlock(\n content=content,\n block_type='import',\n dependencies=set(),\n relevance_score=0.1, # Base score for imports\n token_count=count_tokens(content),\n start_line=1,\n end_line=len(imports_block)\n ))\n \n except SyntaxError:\n # Fallback to basic chunking if AST parsing fails\n return self._basic_chunking(code, search_terms)\n \n return blocks\n \n def _basic_chunking(self, code: str, search_terms: List[str]) -> List[CodeBlock]:\n \"\"\"Fallback chunking method when AST parsing fails\"\"\"\n blocks = []\n lines = code.split('\\n')\n current_block = []\n current_type = None\n start_line = 1\n \n for i, line in enumerate(lines, 1):\n stripped = line.strip()\n \n # Detect block boundaries\n if stripped.startswith(('def ', 'class ', 'async def')):\n if current_block:\n content = '\\n'.join(current_block)\n blocks.append(CodeBlock(\n content=content,\n block_type=current_type or 'unknown',\n dependencies=set(),\n relevance_score=0.0,\n token_count=count_tokens(content),\n start_line=start_line,\n end_line=i-1\n ))\n current_block = [line]\n current_type = 'function' if 'def' in stripped else 'class'\n start_line = i\n elif not stripped and current_block:\n content = '\\n'.join(current_block)\n if content.strip():\n blocks.append(CodeBlock(\n content=content,\n block_type=current_type or 'unknown',\n dependencies=set(),\n relevance_score=0.0,\n token_count=count_tokens(content),\n start_line=start_line,\n end_line=i-1\n ))\n current_block = []\n current_type = None\n start_line = i + 1\n else:\n current_block.append(line)\n \n # Add final block\n if current_block:\n content = '\\n'.join(current_block)\n blocks.append(CodeBlock(\n content=content,\n block_type=current_type or 'unknown',\n dependencies=set(),\n relevance_score=0.0,\n token_count=count_tokens(content),\n start_line=start_line,\n end_line=len(lines)\n ))\n \n # Calculate relevance scores\n for block in blocks:\n block.relevance_score = self._calculate_relevance(block, search_terms)\n \n return blocks\n\n def chunk_code(self, code: str, search_terms: List[str]) -> List[str]:\n \"\"\"Main chunking method that returns relevant code chunks within token limit\"\"\"\n # Parse code into blocks\n blocks = self.parse_code_blocks(code, search_terms)\n \n # Sort blocks by relevance score\n blocks.sort(key=lambda x: x.relevance_score, reverse=True)\n \n # Build chunks respecting token limit\n chunks = []\n current_chunk = []\n current_tokens = 0\n \n for block in blocks:\n if current_tokens + block.token_count > self.max_tokens:\n if current_chunk:\n chunks.append('\\n'.join(chunk.content for chunk in current_chunk))\n current_chunk = []\n current_tokens = 0\n \n # Handle blocks larger than max_tokens\n if block.token_count > self.max_tokens:\n # Split large blocks if possible\n if block.block_type in ['function', 'class']:\n # Keep function signature and important parts\n lines = block.content.split('\\n')\n signature = lines[0]\n body = lines[1:min(len(lines), 10)] # Keep first few lines\n truncated_content = '\\n'.join([signature] + body + [' # ... truncated ...'])\n if count_tokens(truncated_content) <= self.max_tokens:\n chunks.append(truncated_content)\n continue\n \n current_chunk.append(block)\n current_tokens += block.token_count\n \n # Add final chunk\n if current_chunk:\n chunks.append('\\n'.join(chunk.content for chunk in current_chunk))\n \n return chunks","metadata":{"id":"7q5_xlQNrYdu","cellView":"form","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:06:07.289913Z","iopub.execute_input":"2025-03-12T14:06:07.290239Z","iopub.status.idle":"2025-03-12T14:06:07.310803Z","shell.execute_reply.started":"2025-03-12T14:06:07.290215Z","shell.execute_reply":"2025-03-12T14:06:07.310151Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def fetch_file_contents(files_to_search: Dict[str, List[str]]) -> str:\n \"\"\"Enhanced file content fetching using adaptive chunking\"\"\"\n chunker = AdaptiveChunker(max_tokens=1024) # Increased token limit for better context\n output_chunks = []\n \n for path, search_terms in files_to_search.items():\n try:\n with open(path, \"r\", encoding=\"utf-8\", errors=\"replace\") as f:\n code = f.read()\n \n # Get relevant chunks using adaptive chunking\n chunks = chunker.chunk_code(code, search_terms)\n \n # Format chunks with file context\n if chunks:\n output_chunks.append(f\"[file: {path[len(REPO_PATH) + 1:]}]\")\n output_chunks.append(f\"[search terms: {', '.join(search_terms)}]\")\n output_chunks.extend(chunks)\n output_chunks.append(\"\") # Empty line between files\n \n except Exception as e:\n print(f\"Error processing {path}: {str(e)}\")\n \n return \"\\n\".join(output_chunks) if output_chunks else \"\"\n\n# def fetch_file_contents(files_to_search: Dict[str, List[str]], context_lines: int = 12, max_gap: int = 0) -> str:\n# from io import StringIO\n# def find_lines_in_files_with_context(search_map: Dict[str, List[str]], context_lines: int = context_lines) -> List[\n# List[List[Tuple[int, str]]]]:\n# all_matches_per_file: List[List[List[Tuple[int, str]]]] = []\n# for path, terms in search_map.items():\n# if not os.path.isfile(path):\n# all_matches_per_file.append([])\n# continue\n# with open(path, \"r\", encoding=\"utf-8\", errors=\"replace\") as f:\n# lines = f.readlines()\n# file_snippets: List[List[Tuple[int, str]]] = []\n# num_lines = len(lines)\n# for i, line in enumerate(lines, start=1):\n# if any(t in line for t in terms):\n# start_idx = max(1, i - context_lines)\n# end_idx = min(num_lines, i + context_lines)\n# snippet = [(snip_no, lines[snip_no - 1].rstrip(\"\\n\")) for snip_no in range(start_idx, end_idx + 1)]\n# file_snippets.append(snippet)\n# all_matches_per_file.append(file_snippets)\n# return all_matches_per_file\n# def merge_file_snippets(file_snippets: List[List[Tuple[int, str]]], gap: int = 0) -> List[List[Tuple[int, str]]]:\n# intervals: List[Tuple[int, int, List[Tuple[int, str]]]] = []\n# for snippet in file_snippets:\n# if snippet:\n# start_line: int = snippet[0][0]\n# end_line: int = snippet[-1][0]\n# intervals.append((start_line, end_line, snippet))\n# intervals.sort(key=lambda x: x[0])\n# merged: List[Tuple[int, int, List[Tuple[int, str]]]] = []\n# for start, end, snippet in intervals:\n# if not merged:\n# merged.append((start, end, snippet))\n# continue\n# prev_start, prev_end, prev_snippet = merged[-1]\n# if start <= prev_end + gap:\n# new_end = max(end, prev_end)\n# combined = {ln: txt for ln, txt in prev_snippet}\n# for ln, txt in snippet:\n# combined[ln] = txt\n# merged[-1] = (prev_start, new_end, [(ln, combined[ln]) for ln in sorted(combined)])\n# else:\n# merged.append((start, end, snippet))\n# return [x[2] for x in merged]\n\n# def merge_all_snippets(all_files_snips: List[List[List[Tuple[int, str]]]], gap: int = 0) -> List[\n# List[List[Tuple[int, str]]]]:\n# return [merge_file_snippets(snips, gap=gap) for snips in all_files_snips]\n\n# has_any_matches = False\n# context_snippets = find_lines_in_files_with_context(files_to_search, context_lines=context_lines)\n# merged_snips = merge_all_snippets(context_snippets, gap=max_gap)\n# output = StringIO()\n# output.write(\"Sample files created successfully.\\n\\n\")\n# output.write(\"Search Results (by file, merging any overlapping context):\\n\\n\")\n# for (filepath, terms), snippet_list in zip(files_to_search.items(), merged_snips):\n# output.write(f\"[file name]: {filepath[len(REPO_PATH) + 1:]}\\n\")\n# output.write(f\"[terms searched]:\\n{chr(10).join(terms)}\\n\")\n# output.write(\"[file content begin]\\n\")\n# if not snippet_list:\n# output.write(\" No matches found.\\n\")\n# else:\n# has_any_matches = True\n# for idx, snippet in enumerate(snippet_list, start=1):\n# output.write(f\"\\nMatch #{idx}, lines {snippet[0][0]} to {snippet[-1][0]}:\\n\")\n# for ln, txt in snippet:\n# output.write(f\" {ln:3d} | {txt}\\n\")\n# output.write(\"\\n\")\n# output.write(\"[file content end]\\n\\n\")\n# file_content_string = output.getvalue()\n# return file_content_string if has_any_matches else \"\"","metadata":{"id":"7q5_xlQNrYdu","cellView":"form","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:07:13.513177Z","iopub.execute_input":"2025-03-12T14:07:13.513545Z","iopub.status.idle":"2025-03-12T14:07:13.519566Z","shell.execute_reply.started":"2025-03-12T14:07:13.513516Z","shell.execute_reply":"2025-03-12T14:07:13.518916Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# @title TestGen\n# =========================\ndef select_test_file(problem_statement, directory):\n \"\"\"\n Select the appropriate test file from the codebase based on the problem statement.\n\n Args:\n problem_statement (str): The description of the issue to be solved\n directory (str): The root directory of the codebase\n\n Returns:\n str: Path to the most relevant test file\n \"\"\"\n sampling_params = SamplingParams(\n temperature=0.7,\n min_p=0.05,\n skip_special_tokens=True,\n max_tokens=MAX_TOKENS,\n )\n\n prompt = f\"\"\"\n You will be implementing test-driven development for a code repository.\n You need to select the most appropriate test file for the following problem:\n\n {problem_statement}\n\n This is the file directory:\n\n \n {stringify_directory(directory)}\n \n\n Which test file should be selected for test-driven development?\n Return your answer in this format:\n\n path/to/test_file.py\n\n Notes:\n - Return the FULL filepath - exactly as specified in the directory list\n - Choose a test file that would appropriately test the functionality described in the problem\n - If there is no appropriate test file, recommend where a new test file should be created\n \"\"\"\n\n messages = [{\"role\": \"user\", \"content\": prompt}]\n text = tokenizer.apply_chat_template(\n conversation=messages,\n tokenize=False,\n add_generation_prompt=True\n )\n\n print(f\"select_test_file tokens: {len(tokenizer.encode(text))}\")\n request_output = llm.generate(prompts=[text], sampling_params=sampling_params)\n if not request_output:\n return None\n\n response_text = request_output[0].outputs[0].text\n print(f\"select_test_file response tokens: {len(tokenizer.encode(response_text))}\")\n print(response_text)\n\n # Extract test file path\n test_file_match = re.search(r'(.*?)', response_text, re.DOTALL)\n if test_file_match:\n return test_file_match.group(1).strip()\n\n return None\n\ndef identify_issue_functions(problem_statement, test_file_path, directory):\n \"\"\"\n Identify specific functions within the codebase that are related to the issue.\n\n Args:\n problem_statement (str): The description of the issue to be solved\n test_file_path (str): Path to the selected test file (may be new/non-existent)\n directory (str): The root directory of the codebase\n\n Returns:\n list: A list of function names that need to be tested/implemented\n \"\"\"\n sampling_params = SamplingParams(\n temperature=0.7,\n min_p=0.05,\n skip_special_tokens=True,\n max_tokens=MAX_TOKENS,\n )\n\n # Check if the test file exists\n full_path = os.path.join(directory, test_file_path)\n file_exists = os.path.exists(full_path)\n\n # Get the content of the test file if it exists\n test_file_content = \"\"\n if file_exists:\n try:\n with open(full_path, \"r\", encoding=\"utf-8\", errors=\"replace\") as f:\n test_file_content = f.read()\n\n # If the test file is too large, extract only function definitions and imports\n if len(tokenizer.encode(test_file_content)) > 8000:\n # Extract imports and function definitions\n lines = test_file_content.split('\\n')\n important_lines = []\n\n for line in lines:\n if line.strip().startswith(('import ', 'from ', 'def ', 'class ')):\n important_lines.append(line)\n elif line.strip() and any(line.strip().startswith(s) for s in [' ', '\\t']) and important_lines:\n # Include indented lines that are likely part of function definitions\n if important_lines[-1].strip().startswith(('def ', 'class ')):\n important_lines.append(line)\n\n test_file_content = '\\n'.join(important_lines)\n test_file_content += \"\\n\\n# Note: File was truncated to include only function definitions and imports\"\n except Exception as e:\n print(f\"Error reading test file {full_path}: {e}\")\n\n # Common function to truncate source content when needed\n def truncate_source_content(content):\n if len(tokenizer.encode(content)) > 5000:\n # Extract imports and function signatures\n lines = content.split('\\n')\n important_lines = []\n in_function_def = False\n\n for line in lines:\n if line.strip().startswith(('import ', 'from ')):\n important_lines.append(line)\n elif line.strip().startswith('def '):\n important_lines.append(line)\n in_function_def = True\n elif in_function_def and line.strip() and any(c.isspace() for c in line[:2]):\n # Only include first few lines of function body\n if len([l for l in important_lines if l.strip() and any(c.isspace() for c in l[:2])]) < 3:\n important_lines.append(line)\n else:\n in_function_def = False\n elif not line.strip() or not any(c.isspace() for c in line[:2]):\n in_function_def = False\n\n content = '\\n'.join(important_lines)\n content += \"\\n\\n# Note: File was truncated to include only function definitions and imports\"\n return content\n\n # If test file doesn't exist, adjust the prompt to analyze the module/feature\n # based on the problem statement and file path\n if not file_exists:\n # Extract module name from the test file path\n module_name = os.path.basename(test_file_path).replace('test_', '').replace('.py', '')\n\n # Find existing source files that might be related to this module\n potential_source_files = []\n module_pattern = re.compile(rf\"{module_name}\\.py$\")\n\n for root, dirs, files in os.walk(directory):\n for file in files:\n if module_pattern.search(file) and not file.startswith('test_'):\n rel_path = os.path.relpath(os.path.join(root, file), directory)\n potential_source_files.append(rel_path)\n\n # Limit to top 3 most relevant files\n potential_source_files = potential_source_files[:3]\n\n # Get content of potential source files\n source_contents = {}\n for src_file in potential_source_files:\n src_path = os.path.join(directory, src_file)\n try:\n with open(src_path, \"r\", encoding=\"utf-8\", errors=\"replace\") as f:\n content = f.read()\n # Truncate large source files\n source_contents[src_file] = truncate_source_content(content)\n except Exception as e:\n print(f\"Error reading {src_path}: {e}\")\n\n # Prepare source files string for the prompt, limiting total tokens\n source_files_str = \"\"\n total_tokens = 0\n\n for file_path, content in source_contents.items():\n file_content_str = f\"\\nFile: {file_path}\\n```python\\n{content}\\n```\\n\"\n file_tokens = len(tokenizer.encode(file_content_str))\n\n if total_tokens + file_tokens > 16000: # Reserve space for the rest of the prompt\n source_files_str += \"\\n# Additional source files were omitted due to token limitations\"\n break\n\n source_files_str += file_content_str\n total_tokens += file_tokens\n\n prompt = f\"\"\"\n Based on the following problem statement, identify the specific functions that need to be tested:\n\n Problem statement:\n {problem_statement}\n\n You need to create a new test file at: {test_file_path}\n\n {f'Here are some potentially related source files that might help identify relevant functions:' if source_contents else ''}\n {source_files_str}\n\n List the function names that need to be tested or implemented to address the problem.\n Return your answer in this format:\n\n \n function_name_1\n function_name_2\n \n\n Notes:\n - Only include functions directly related to the issue\n - Include both existing functions that need modification and new functions that should be created\n - If needed, suggest new function names based on the problem description\n \"\"\"\n else:\n # Original prompt for existing test files\n prompt = f\"\"\"\n Based on the following problem statement and test file, identify the specific functions that need to be tested:\n\n Problem statement:\n {problem_statement}\n\n Test file content:\n ```python\n {test_file_content}\n ```\n\n List the function names that need to be tested or implemented to address the problem.\n Return your answer in this format:\n\n \n function_name_1\n function_name_2\n \n\n Notes:\n - Only include functions directly related to the issue\n - Include both existing functions that need modification and new functions that should be created\n \"\"\"\n\n # Check prompt size and truncate if necessary\n prompt_tokens = len(tokenizer.encode(prompt))\n if prompt_tokens > 30000: # Provide some buffer under the 32768 limit\n print(f\"Warning: Prompt too large ({prompt_tokens} tokens). Truncating to fit within limits.\")\n prompt = f\"\"\"\n Based on the following problem statement, identify the specific functions that need to be tested:\n\n Problem statement:\n {problem_statement}\n\n You need to {'' if file_exists else 'create a new'} test file at: {test_file_path}\n\n Using your understanding of the problem, list the function names that need to be tested or implemented.\n Return your answer in this format:\n\n \n function_name_1\n function_name_2\n \n\n Notes:\n - Only include functions directly related to the issue\n - Include both existing functions that need modification and new functions that should be created\n - If needed, suggest new function names based on the problem description\n \"\"\"\n\n messages = [{\"role\": \"user\", \"content\": prompt}]\n text = tokenizer.apply_chat_template(\n conversation=messages,\n tokenize=False,\n add_generation_prompt=True\n )\n\n prompt_tokens = len(tokenizer.encode(text))\n print(f\"identify_issue_functions tokens: {prompt_tokens}\")\n\n if prompt_tokens > 32768:\n print(\"Error: Prompt still exceeds token limits after truncation. Using minimal prompt.\")\n # Minimal fallback prompt\n minimal_prompt = f\"\"\"\n Given this problem statement: \"{problem_statement}\"\n\n Suggest function names that would need to be implemented or tested.\n Return only in this format:\n \n name1\n name2\n \n \"\"\"\n messages = [{\"role\": \"user\", \"content\": minimal_prompt}]\n text = tokenizer.apply_chat_template(\n conversation=messages,\n tokenize=False,\n add_generation_prompt=True\n )\n\n request_output = llm.generate(prompts=[text], sampling_params=sampling_params)\n if not request_output:\n return []\n\n response_text = request_output[0].outputs[0].text\n print(f\"identify_issue_functions response tokens: {len(tokenizer.encode(response_text))}\")\n\n # Extract function names\n functions = []\n function_matches = re.findall(r'(.*?)', response_text, re.DOTALL)\n for match in function_matches:\n functions.append(match.strip())\n\n return functions\n\ndef generate_test_function(problem_statement, test_file_path, functions, directory):\n \"\"\"\n Generate a test function based on the issue description.\n\n Args:\n problem_statement (str): The description of the issue to be solved\n test_file_path (str): Path to the selected test file\n functions (list): List of function names to test\n directory (str): The root directory of the codebase\n\n Returns:\n str: Generated test function code\n \"\"\"\n sampling_params = SamplingParams(\n temperature=0.7,\n min_p=0.05,\n skip_special_tokens=True,\n max_tokens=MAX_TOKENS,\n )\n\n # Function to truncate content when needed\n def truncate_content(content, max_tokens=5000):\n if len(tokenizer.encode(content)) > max_tokens:\n # Extract imports and function definitions that match our target functions\n lines = content.split('\\n')\n important_lines = []\n in_relevant_function = False\n function_indent = 0\n\n # First pass: extract imports and class definitions\n for line in lines:\n stripped = line.strip()\n if stripped.startswith(('import ', 'from ')):\n important_lines.append(line)\n elif stripped.startswith('class '):\n important_lines.append(line)\n\n # Second pass: extract relevant function definitions\n current_function = None\n for line in lines:\n stripped = line.strip()\n indent = len(line) - len(line.lstrip())\n\n # Start of function definition\n if stripped.startswith('def '):\n function_name = stripped[4:].split('(')[0].strip()\n if function_name in functions:\n in_relevant_function = True\n function_indent = indent\n current_function = function_name\n important_lines.append(line)\n else:\n in_relevant_function = False\n\n # Inside relevant function\n elif in_relevant_function:\n if (not stripped and indent <= function_indent) or (stripped and indent <= function_indent and not stripped.startswith('#')):\n in_relevant_function = False\n else:\n important_lines.append(line)\n\n content = '\\n'.join(important_lines)\n content += \"\\n\\n# Note: File was truncated to include only relevant functions and imports\"\n return content\n\n # First, get the content of any existing source files that implement the functions\n source_files = find_source_files_for_functions(functions, directory)\n source_contents = {}\n\n total_source_tokens = 0\n max_total_source_tokens = 14000 # Reserve tokens for the rest of the prompt\n\n for file_path in source_files:\n full_path = os.path.join(directory, file_path)\n if os.path.exists(full_path):\n try:\n with open(full_path, \"r\", encoding=\"utf-8\", errors=\"replace\") as f:\n content = f.read()\n\n # Truncate large files to focus on relevant functions\n truncated_content = truncate_content(content)\n file_tokens = len(tokenizer.encode(truncated_content))\n\n # Check if adding this file would exceed our token budget\n if total_source_tokens + file_tokens > max_total_source_tokens:\n print(f\"Skipping file {file_path} due to token limitations\")\n if not source_contents: # If we haven't added any files yet, add at least one\n source_contents[file_path] = truncate_content(content, max_tokens=max_total_source_tokens)\n continue\n\n source_contents[file_path] = truncated_content\n total_source_tokens += file_tokens\n except Exception as e:\n print(f\"Error reading {full_path}: {e}\")\n\n # Get test file content\n test_file_content = \"\"\n full_test_path = os.path.join(directory, test_file_path)\n if os.path.exists(full_test_path):\n try:\n with open(full_test_path, \"r\", encoding=\"utf-8\", errors=\"replace\") as f:\n content = f.read()\n\n # If test file is too large, extract important parts\n if len(tokenizer.encode(content)) > 8000:\n # Focus on imports, test class definitions, and existing test functions\n lines = content.split('\\n')\n important_lines = []\n in_test_class = False\n class_indent = 0\n\n for line in lines:\n stripped = line.strip()\n indent = len(line) - len(line.lstrip())\n\n if stripped.startswith(('import ', 'from ')):\n important_lines.append(line)\n elif stripped.startswith('class ') and 'Test' in stripped:\n important_lines.append(line)\n in_test_class = True\n class_indent = indent\n elif in_test_class and stripped.startswith('def test_'):\n important_lines.append(line)\n # Add a few lines of the test function body\n next_lines = []\n for i in range(5): # Add up to 5 representative lines\n if i + lines.index(line) + 1 < len(lines):\n next_line = lines[lines.index(line) + i + 1]\n next_stripped = next_line.strip()\n next_indent = len(next_line) - len(next_line.lstrip())\n if next_indent > indent:\n next_lines.append(next_line)\n else:\n break\n important_lines.extend(next_lines)\n important_lines.append(' # ... rest of test function ...')\n elif in_test_class and (not stripped or (indent <= class_indent and stripped)):\n in_test_class = False\n\n test_file_content = '\\n'.join(important_lines)\n test_file_content += \"\\n\\n# Note: Test file was truncated to show representative test functions\"\n else:\n test_file_content = content\n except Exception as e:\n print(f\"Error reading test file {full_test_path}: {e}\")\n\n # Create source files string\n source_files_str = \"\"\n for file_path, content in source_contents.items():\n source_files_str += f\"\\nFile: {file_path}\\n```python\\n{content}\\n```\\n\"\n\n prompt = f\"\"\"\n You are implementing test-driven development for this problem:\n\n {problem_statement}\n\n These are the functions that need to be tested:\n {', '.join(functions)}\n\n The current test file looks like:\n ```python\n {test_file_content}\n ```\n\n Here are the relevant source files that might implement these functions:\n {source_files_str}\n\n Generate a test function for the test file that would verify the correct behavior after fixing the problem.\n The test should follow these principles:\n 1. It should test the specific functionality mentioned in the problem statement\n 2. It should match the style of existing tests in the file\n 3. It should be comprehensive enough to verify the fix works correctly\n 4. It should include appropriate assertions\n\n Return your test function within tags:\n Return only in this format:\n \n def test_function_name():\n # Test implementation here\n ...\n \n \"\"\"\n\n # Check prompt size and truncate if necessary\n prompt_tokens = len(tokenizer.encode(prompt))\n print(f\"Initial generate_test_function prompt tokens: {prompt_tokens}\")\n\n if prompt_tokens > 30000: # Safe threshold below the 32768 limit\n print(f\"Warning: Prompt too large ({prompt_tokens} tokens). Using simplified prompt.\")\n # Create a simplified prompt with just the essential information\n simplified_prompt = f\"\"\"\n You are implementing test-driven development for this problem:\n\n {problem_statement}\n\n These are the functions that need to be tested:\n {', '.join(functions)}\n\n {f'The test file is located at: {test_file_path}' if test_file_content else ''}\n\n {'Here is a brief overview of the existing test style:' if test_file_content else ''}\n {test_file_content[:500] + '...' if len(test_file_content) > 500 else test_file_content}\n\n Generate a test function that would verify the correct behavior after fixing the problem.\n The test should follow these principles:\n 1. It should test the specific functionality mentioned in the problem statement\n 2. It should match the style of existing tests if possible\n 3. It should be comprehensive enough to verify the fix works correctly\n 4. It should include appropriate assertions\n\n Return your test function within tags:\n Return only in this format:\n \n def test_function_name():\n # Test implementation here\n ...\n \n \"\"\"\n\n prompt = simplified_prompt\n\n messages = [{\"role\": \"user\", \"content\": prompt}]\n text = tokenizer.apply_chat_template(\n conversation=messages,\n tokenize=False,\n add_generation_prompt=True\n )\n\n prompt_tokens = len(tokenizer.encode(text))\n print(f\"generate_test_function tokens: {prompt_tokens}\")\n\n # Final fallback if still too large\n if prompt_tokens > 32768:\n print(\"Error: Prompt still exceeds token limits after simplification. Using minimal prompt.\")\n minimal_prompt = f\"\"\"\n Problem: {problem_statement}\n\n Functions to test: {', '.join(functions)}\n\n Write a Python test function to verify these functions work correctly after fixing the problem.\n\n Return only the test function code within tags.\n\n Return only in this format:\n test_code>\n def test_function_name():\n # Test implementation here\n ...\n \n \"\"\"\n messages = [{\"role\": \"user\", \"content\": minimal_prompt}]\n text = tokenizer.apply_chat_template(\n conversation=messages,\n tokenize=False,\n add_generation_prompt=True\n )\n\n request_output = llm.generate(prompts=[text], sampling_params=sampling_params)\n if not request_output:\n return None\n\n response_text = request_output[0].outputs[0].text\n print(f\"generate_test_function response tokens: {len(tokenizer.encode(response_text))}\")\n\n # Extract test code\n test_code_match = re.search(r'(.*?)', response_text, re.DOTALL)\n test_code_match = test_code_match if test_code_match else re.search(r'```(?:\\w*)?\\s*(.*?)```', response_text, re.DOTALL)\n if test_code_match:\n return test_code_match.group(1).strip()\n\n print(response_text)\n print(test_code_match)\n\n return None\n\ndef find_source_files_for_functions(functions, directory):\n \"\"\"\n Find source files that implement the specified functions.\n\n Args:\n functions (list): List of function names to search for\n directory (str): The root directory of the codebase\n\n Returns:\n list: Paths to relevant source files\n \"\"\"\n relevant_files = []\n\n # Create a regex pattern to search for function definitions\n function_patterns = [re.compile(rf\"def\\s+{func}\\s*\\(\", re.IGNORECASE) for func in functions]\n\n for root, dirs, files in os.walk(directory):\n for file in files:\n if file.endswith('.py') and not file.startswith('test_'):\n file_path = os.path.join(root, file)\n try:\n with open(file_path, 'r', encoding='utf-8', errors='replace') as f:\n content = f.read()\n\n # Check if any of the functions are defined in this file\n for pattern in function_patterns:\n if pattern.search(content):\n # Return path relative to directory\n rel_path = os.path.relpath(file_path, directory)\n relevant_files.append(rel_path)\n break\n except Exception as e:\n print(f\"Error reading {file_path}: {e}\")\n\n return relevant_files\n\ndef implement_auto_tdd(problem_statement, directory):\n \"\"\"\n Main function to implement Auto-TDD workflow.\n\n Args:\n problem_statement (str): The description of the issue to be solved\n directory (str): The root directory of the codebase\n\n Returns:\n dict: Results of the Auto-TDD process including selected test file and generated test code\n \"\"\"\n results = {\n 'test_file': None,\n 'functions': [],\n 'test_code': None\n }\n\n # Step 1: Select the relevant test file\n test_file = select_test_file(problem_statement, directory)\n print(\"============================test file = \", test_file)\n results['test_file'] = test_file\n \n if not test_file:\n print(\"No suitable test file found.\")\n return results\n\n # Step 2: Identify functions related to the issue\n functions = identify_issue_functions(problem_statement, test_file, directory)\n results['functions'] = functions\n if not functions:\n print(\"No relevant functions identified.\")\n return results\n\n # Step 3: Generate test function\n test_code = generate_test_function(problem_statement, test_file, functions, directory)\n results['test_code'] = test_code\n if not test_code:\n print(\"Failed to generate test code.\")\n return results\n\n print(f\"Auto-TDD completed successfully!\")\n print(f\"Test file: {test_file}\")\n print(f\"Functions to implement/modify: {', '.join(functions)}\")\n print(f\"Generated test code: \\n{test_code}\")\n\n return results","metadata":{"cellView":"form","id":"-TSW8m_w5hDl","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:08:01.572066Z","iopub.execute_input":"2025-03-12T14:08:01.572369Z","iopub.status.idle":"2025-03-12T14:08:02.010241Z","shell.execute_reply.started":"2025-03-12T14:08:01.572347Z","shell.execute_reply":"2025-03-12T14:08:02.009567Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"cot_prompt = (\"\"\"\n\nYou will review the provided GitHub issue/problem statement and relevant files carefully.\nGitHub Issue:{problem_statement}\n\nRelevant Code Files: {file_content_string}\nTest cases to pass: {test_case_suite}\n\nYour Task is to understand the Issue & Plan the Fix:\nLet's reason step by step:\n1️ Understand the Issue\n- Summarize the problem in your own words.\n- Identify the specific error, inefficiency, or missing logic causing the issue.\n\n2️ Analyze the files and test cases\n- Examine the provided files and explain which functions or lines of code are responsible for this issue.\n- Do you have all the files and functions you need to patch this issue?\n- If relevant, describe dependencies between different files or functions and their effects.\n\n3️ Plan the Fix\n- Before writing any code, describe what modifications are needed.\n- If a function needs to be updated or a new function is required, explain why.\n- Clearly outline the steps the patch should take to resolve the issue.\n- Insepct the test cases to ensure proposed solution meets test requirement. Consider edge cases.\n\nOutput Format (Strict JSON-like structure) Format your output according to this structure and example:\n json\n {{\n \"issue_summary\": \"Concise summary of the issue.\",\n \"root_cause_analysis\": \"Explanation of the bug, why it occurs, and which parts of the code are responsible.\",\n \"affected_files\": [\n {{\n \"file_name\": \"file1.py\",\n \"functions_impacted\": [\"function_A\", \"function_B\"],\n \"code_analysis\": \"Explanation of what function_A and function_B do and why they are causing the issue.\"\n }}\n ],\n \"fix_plan\": {{\n \"high_level_changes\": [\"Modify function_A\", \"Refactor function_B\"],\n \"detailed_steps\": [\n {{ \"step\": 1, \"description\": \"Check for empty inputs in function_A.\" }},\n {{ \"step\": 2, \"description\": \"Optimize function_B by caching previous results.\" }}\n ]\n }}\n }}\n\n\"\"\"\n)\n\n\npatch_prompt = \"\"\"\n ### Issue Summary & Fix Plan ###\n {step_1_output}\n\n ### Task: Generate a Patch ###\n\n Now, use your analysis above to generate a code patch that fixes the issue.\n\n **Follow these requirements:**\n - Identify the lines of code that need to be modified.\n - Generate a **GitHub-style diff patch** with only the necessary changes.\n - **Do NOT** repeat explanations—only return the patch.\n\n ---\n **Output Format (GitHub-style diff)**\n You must follow this GitHub-style diff format in your output:\n ```diff\n --- a/first.txt\n +++ b/first.txt\n @@ -1,3 +1,3 @@\n start\n -first change\n +new first change\n middle\n --- a/second.txt\n +++ b/second.txt\n @@ -1,3 +1,3 @@\n beginning\n -old line\n +new line\n end\n ```\n\n Reminder:\n - Put your diff within ```diff and ``` and make sure the diff is valid.\n - Do not edit the test files.\n\n # Generate the patch\n\"\"\"\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:08:12.056309Z","iopub.execute_input":"2025-03-12T14:08:12.056646Z","iopub.status.idle":"2025-03-12T14:08:12.060687Z","shell.execute_reply.started":"2025-03-12T14:08:12.056622Z","shell.execute_reply":"2025-03-12T14:08:12.059774Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# @title PatchGen & PatchEval\n# =========================\ndef extract_patch_string(text: str) -> Optional[str]:\n pattern = r\"\\n```diff\\n(.*?)\\n```\"\n matches = re.findall(pattern, text, re.DOTALL)\n return (matches[-1] + \"\\n\") if matches else None\n\n\npatching_prompt: str = (\n \"\"\"\n You will be implementing a git diff patch to solve an issue with the code repository.\n This is the problem statement.\n\n {problem_statement}\\n\n\n These are the files that is thought to be relevant\\n\n\n {file_content_string}\\n\n\n Write a git diff within ```diff and ``` that fully fixes the problem.\n The git diff should not cause other tests to fail.\n\n IMPORTANT: Follow the format given\n Example: \n ```diff\n --- a/first.txt\n +++ b/first.txt\n @@ -1,3 +1,3 @@\n start\n -first change\n +new first change\n middle\n --- a/second.txt\n +++ b/second.txt\n @@ -1,3 +1,3 @@\n beginning\n -old line\n +new line\n end\n ```\n\n Reminder:\n - Put your diff within ```diff and ``` and make sure the diff is valid.\n - Only the last diff printed will be considered.\n - Do not edit the test files.\n \"\"\".strip()\n)\n\n\ndef count_total_tokens(messages):\n \"\"\"Estimates total token count in conversation history.\"\"\"\n return sum(count_tokens(msg[\"content\"]) for msg in messages)\n\ndef trim_old_messages(messages, max_tokens=MAX_TOKENS):\n \"\"\"Trims older messages to fit within the token limit.\"\"\"\n while count_total_tokens(messages) > max_tokens:\n messages.pop(0) # Remove the oldest message\n return messages\n\n\n\n# def get_patch_string(problem_statement: str, file_content_strings: List[str], test_case_suite: List[str]) -> Tuple[str, Optional[str]]:\ndef get_patch_string(problem_statement: str, file_content_strings: List[str], test_case_suite: List[str], repo_archive: io.BytesIO, directory: str) -> Tuple[str, Optional[str]]:\n \n \"\"\"Generates a git diff patch for a given problem statement and related files.\"\"\"\n \n sampling_params = SamplingParams(\n temperature=1, \n min_p=0.01, \n skip_special_tokens=True, \n max_tokens=MAX_TOKENS)\n \n # Collect only non-empty file contents\n relevant_files = [fcs[:30000] for fcs in file_content_strings if fcs.strip()]\n \n if not relevant_files:\n return \"\", None # No relevant files, return empty response\n\n # Get historical fixes for similar issues\n historical_fixes = []\n for issue_key in history_dict.keys():\n # Get historical heuristics for this issue\n file_path = get_from_history(issue_key, HistoryDict.FILE_PATH)\n function_name = get_from_history(issue_key, HistoryDict.FUNCTION_NAME)\n \n if file_path and function_name:\n heuristics = get_historical_heuristics(\n repo_archive=repo_archive,\n history_dict=history_dict,\n archive_path=directory,\n file_path=file_path,\n function_name=function_name\n )\n \n # Add relevant historical fixes\n if heuristics[HistoryHeuristic.FN_PAIR.name]:\n historical_fixes.extend(heuristics[HistoryHeuristic.FN_PAIR.name])\n if heuristics[HistoryHeuristic.FL_DIFF.name]:\n historical_fixes.extend(heuristics[HistoryHeuristic.FL_DIFF.name])\n\n \n # Format the file contents into a single string\n formatted_file_contents = \"\\n\\n\".join(\n [f\"### File {idx+1}\\n{content}\" for idx, content in enumerate(relevant_files)]\n )\n\n # Add historical fix patterns if available\n if historical_fixes:\n formatted_file_contents += \"\\n\\n### Historical Fix Patterns\\n\"\n formatted_file_contents += \"\\n\".join(\n f\"Previous fix {idx+1}:\\n{fix}\" \n for idx, fix in enumerate(historical_fixes[:5]) # Limit to 5 most recent fixes\n )\n \n # Create a single prompt instead of multiple independent ones\n messages = [\n {\"role\": \"user\", \"content\": cot_prompt.format(\n problem_statement=problem_statement[:20000], \n file_content_string=formatted_file_contents, \n test_case_suite=test_case_suite\n )}\n ]\n\n # Tokenize and prepare prompt for generation\n prompt_texts = [\n tokenizer.apply_chat_template(\n conversation=[message], \n tokenize=False, \n add_generation_prompt=True)\n for message in messages\n ]\n # print(\"get_patch_string token count:\", count_tokens(prompt_texts[0]))\n # print(prompt_texts[0])\n # Generate response from LLM\n request_outputs: RequestOutput = llm.generate(prompt_texts, sampling_params=sampling_params)\n \n response_texts = [request_output.outputs[0].text for request_output in request_outputs]\n response_text = response_texts[0]\n # print(\"get_patch_string response token count:\", count_tokens(response_text))\n # print(response_text)\n # Extract the patch string\n patch_string = extract_patch_string(response_text)\n\n return response_text, patch_string\n\n\nverifying_prompt: str = (\n \"\"\"\nThis is the problem statement.\n\n{problem_statement}\n\nThese are the files that is thought to be relevant, which may not be complete.\n\n{file_content_string}\n\nThis is the proposed patch to fix the problem.\n\n{patch_string}\n\nEvaluate whether the patch works\n- The patch fully fixes the problem described in the problem statement.\n- The patch does not cause side effects and make any other tests fail.\n\nEnd your response with exactly either of\n- , this fixes the problem.\n- , this does not fix the problem.\n\nReminder\n- Only evaluate, do not provide suggestion on how to fix.\n- Remember to write exactly either of or in the last line\n\"\"\".strip()\n)\n\n\ndef get_verification(problem_statement: str, file_content_strings: List[str], patch_strings: List[Optional[str]],\n repo_path: str) -> Tuple[List[List[str]], List[List[bool]]]:\n patch_strings = \"\" if patch_strings is None else patch_strings\n file_content_strings = \"\" if file_content_strings is None else file_content_strings\n # assert len(file_content_strings) == len(patch_strings)\n sampling_params = SamplingParams(temperature=0.6, min_p=0.01, skip_special_tokens=True, max_tokens=MAX_TOKENS)\n inference_idx_to_input_idx = [\n input_idx for _ in range(VALIDATION_COPY_COUNT) for input_idx, patch_string in enumerate(patch_strings)\n if patch_string is not None\n and is_valid_patch_format(patch_string)\n and patch_dry_run_succeeds(patch_string, repo_path)\n and patch_string != \"\"\n ]\n print(inference_idx_to_input_idx)\n list_of_messages = [\n [{\"role\": \"user\", \"content\": verifying_prompt.format(problem_statement=problem_statement[:20000],\n file_content_string=file_content_strings[input_idx][:30000],\n patch_string=patch_strings[input_idx])}]\n for input_idx in inference_idx_to_input_idx\n ]\n prompt_texts = [\n tokenizer.apply_chat_template(conversation=msgs, tokenize=False, add_generation_prompt=True) + \"\\n\"\n for msgs in list_of_messages\n ]\n print(\"get_verification\", [count_tokens(text) for text in prompt_texts])\n request_outputs: List[RequestOutput] = llm.generate(prompt_texts, sampling_params=sampling_params)\n response_texts = [ro.outputs[0].text for ro in request_outputs]\n print(\"get_verification\", [count_tokens(text) for text in response_texts])\n completion_texts = [pt + rt for pt, rt in zip(prompt_texts, response_texts)]\n judgments_flattened = [\"\" in rt for rt in response_texts]\n print(judgments_flattened)\n judgments_aggregated = [[] for _ in file_content_strings]\n completion_text_aggregated = [[] for _ in patch_strings]\n for idx, (comp_text, judgement) in enumerate(zip(completion_texts, judgments_flattened)):\n input_idx = inference_idx_to_input_idx[idx]\n completion_text_aggregated[input_idx].append(comp_text)\n judgments_aggregated[input_idx].append(judgement)\n print(judgments_aggregated)\n return completion_text_aggregated, judgments_aggregated\n\n\n@cache\ndef is_valid_patch_format(patch_string: str) -> bool:\n if not isinstance(patch_string, str):\n return False\n try:\n patch_set = unidiff.PatchSet(patch_string)\n return len(patch_set) > 0\n except Exception:\n return False\n\n\n\n@cache\ndef patch_dry_run_succeeds(patch_string: str, repo_path: str = REPO_PATH, timeout: int = 60) -> bool:\n with open(\"patch.txt\", \"w\") as f:\n f.write(patch_string)\n patch_path = \"/kaggle/working/patch.txt\"\n cmd = f\"patch --quiet --dry-run -p1 -i {patch_path} -d {repo_path}\"\n try:\n subprocess.run(cmd, shell=True, check=True, timeout=timeout)\n return True\n except subprocess.CalledProcessError:\n return False\n\n\ndef choose_patch_string(patch_strings: List[Optional[str]], judgments_aggregated: List[List[bool]], repo_path: str) -> \\\n Tuple[List[int], Optional[str]]:\n best_score = -4\n best_patch_string = None\n scores = []\n for judgments, patch_string in zip(judgments_aggregated, patch_strings):\n if patch_string is None:\n score = -3\n scores.append(score)\n continue\n if not is_valid_patch_format(patch_string):\n score = -2\n scores.append(score)\n continue\n if not patch_dry_run_succeeds(patch_string, repo_path):\n score = -1\n scores.append(score)\n continue\n score = judgments.count(True)\n scores.append(score)\n if score > best_score:\n best_score = score\n best_patch_string = patch_string\n return scores, best_patch_string","metadata":{"id":"mWAVGNtorhVm","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:08:29.097935Z","iopub.execute_input":"2025-03-12T14:08:29.098252Z","iopub.status.idle":"2025-03-12T14:08:29.115414Z","shell.execute_reply.started":"2025-03-12T14:08:29.098228Z","shell.execute_reply":"2025-03-12T14:08:29.114784Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# @title Inference / Predict Function\n# =========================\n# def process_issue(problem_statement: str, directory: str) -> Optional[str]:\ndef process_issue(problem_statement: str, directory: str, repo_archive: io.BytesIO) -> Optional[str]:\n\n global issue_number_count\n current_issue = f\"issue_{issue_number_count}\"\n issue_number_count += 1\n \n is_valid_patch_format.cache_clear()\n patch_dry_run_succeeds.cache_clear()\n\n # # 1. parse_issue_context\n # issue_context = parse_issue_context(problem_statement)\n\n # # 2. Apply basic preprocessing and create enriched context with adaptive chunking\n # processed_issue = basic_preprocessing({\"description\": problem_statement})\n \n # refined_statement = processed_issue[\"description_processed\"] # For semantic analysis\n # rephrased_statement = processed_issue[\"description_rephrased\"] # For clarity\n # summarized_statement = processed_issue[\"description_summarized\"] # For token efficiency\n\n # 1. Parse issue context for key information\n issue_context = parse_issue_context(problem_statement)\n\n # 2. Create program graphs for repository\n print(\"Creating program graphs for repository...\")\n graph_data = analyze_repo(directory)\n add_to_history(current_issue, HistoryDict.AST, str(graph_data))\n \n # Extract relevant files based on program graph analysis\n relevant_files = []\n for file_path, graphs in graph_data.items():\n # Check if file has functions/variables mentioned in issue\n for func in issue_context['functions']:\n if any(f.name == func for f in graphs.function_defs):\n relevant_files.append(file_path)\n break\n \n # Check data dependencies that might be relevant\n if graphs.metadata['data_dependencies'] > 0:\n for var in issue_context['variables']:\n if var in graphs.var_defs:\n relevant_files.append(file_path)\n break\n\n # 3. Apply basic preprocessing and create enriched context with adaptive chunking\n processed_issue = basic_preprocessing({\"description\": problem_statement})\n # Store preprocessed data in history\n add_to_history(current_issue, HistoryDict.CLEANED_ISSUE, processed_issue[\"description_processed\"])\n add_to_history(current_issue, HistoryDict.REFRAMED_ISSUE, processed_issue[\"description_rephrased\"])\n add_to_history(current_issue, HistoryDict.SUMMARIZED_ISSUE, processed_issue[\"description_summarized\"])\n\n # Get historical heuristics for similar issues\n historical_insights = []\n for file_path in issue_context['file_references']:\n for func in issue_context['functions']:\n heuristics = get_historical_heuristics(\n repo_archive=repo_archive,\n history_dict=history_dict,\n archive_path=directory,\n file_path=file_path,\n function_name=func\n )\n \n if heuristics:\n insight = {\n 'file': file_path,\n 'function': func,\n 'co_evolved_functions': heuristics[HistoryHeuristic.CFN_MODIFIED.name],\n 'related_files': heuristics[HistoryHeuristic.FLN_ALL.name],\n 'previous_fixes': heuristics[HistoryHeuristic.FN_PAIR.name],\n 'diff_patches': heuristics[HistoryHeuristic.FL_DIFF.name]\n }\n historical_insights.append(insight)\n\n ######\n # Create context sections with priority levels\n context_sections = [\n ContextSection(\"Original Problem\", problem_statement, 10), # Highest priority\n ContextSection(\"Key Points Summary\", processed_issue[\"description_summarized\"], 9),\n ContextSection(\"Error Messages\", \n ', '.join(issue_context['error_messages']) if issue_context['error_messages'] else 'None', \n 8 if issue_context['error_messages'] else 3),\n ContextSection(\"Functions\", \n ', '.join(issue_context['functions']) if issue_context['functions'] else 'None',\n 7 if issue_context['functions'] else 2),\n ContextSection(\"File References\",\n ', '.join(issue_context['file_references']) if issue_context['file_references'] else 'None',\n 6 if issue_context['file_references'] else 2),\n ContextSection(\"Historical Context\",\n format_historical_insights(historical_insights) if historical_insights else 'No relevant history found',\n 8 if historical_insights else 1),\n ContextSection(\"Refined Analysis\", processed_issue[\"description_processed\"], 5),\n ContextSection(\"Rephrased for Clarity\", processed_issue[\"description_rephrased\"], 4),\n ContextSection(\"Variables\",\n ', '.join(issue_context['variables']) if issue_context['variables'] else 'None',\n 3 if issue_context['variables'] else 1)\n ]\n \n # Calculate token count for each section using the new method\n section_tokens = [(section, section.token_count())\n for section in context_sections]\n \n # Sort sections by priority and token efficiency (priority/tokens)\n sorted_sections = sorted(\n section_tokens,\n key=lambda x: (x[0].priority, -x[1]), # Sort by priority (high to low) then tokens (low to high)\n reverse=True\n )\n \n # Build enriched context within token limit\n total_tokens = 0\n included_sections = []\n excluded_sections = []\n \n for section, tokens in sorted_sections:\n if total_tokens + tokens <= ENRICHED_CONTEXT_TOKENS:\n included_sections.append(section.format())\n total_tokens += tokens\n logging.info(f\"Including section '{section.title}' ({tokens} tokens)\")\n else:\n excluded_sections.append(f\"{section.title} ({tokens} tokens)\")\n logging.info(f\"Excluding section '{section.title}' due to token limit\")\n \n if excluded_sections:\n logging.warning(f\"Excluded sections due to token limit: {', '.join(excluded_sections)}\")\n \n # Combine sections into enriched context\n enriched_context = \"\\n\\n\".join(included_sections)\n logging.info(f\"Total tokens in enriched context: {total_tokens}\")\n\n ######\n \n # Only if repo not in history_dict\n # 3. AST\n repo_key = repo_archive\n if repo_key in history_dict:\n asts_path = history_dict[repo_key].get(\"asts_path\")\n # repo_asts = create_asts_for_repo(repo_archive)\n if asts_path and os.path.exists(asts_path):\n repo_asts = load_asts_from_file(asts_path)\n else:\n repo_asts = create_asts_for_repo(repo_path)\n asts_path = f\"{repo_key}_asts.pkl\"\n save_asts_to_file(repo_asts, asts_path)\n history_dict[repo_key] = {\"asts_path\": asts_path}\n else:\n print(f\"Repo '{repo_key}' not found in history_dict. Creating ASTs.\")\n repo_asts = create_asts_for_repo(repo_path)\n asts_path = f\"{repo_key}_asts.pkl\"\n save_asts_to_file(repo_asts, asts_path)\n history_dict[repo_key] = {\"asts_path\": asts_path}\n\n # Combine program graph analysis with LLM-based selection\n directory_string = stringify_directory(directory)\n \n # Add program graph insights to guide file selection\n graph_insights = []\n for file_path in relevant_files:\n graphs = graph_data[file_path]\n insight = {\n 'file': file_path,\n 'functions': [f.name for f in graphs.function_defs],\n 'data_deps': graphs.metadata['data_dependencies'],\n 'control_flows': graphs.metadata['control_flows']\n }\n graph_insights.append(insight)\n \n sel_completion_texts, file_queries = get_selection_query(\n directory_string, \n enriched_context,\n relevant_files=relevant_files,\n graph_insights=graph_insights # Pass both relevant files and graph insights\n )\n # directory_string = stringify_directory(directory)\n # # print(\"==============directory_string\", directory_string)\n # # sel_completion_texts, file_queries = get_selection_query(directory_string, problem_statement)\n # sel_completion_texts, file_queries = get_selection_query(directory_string, enriched_context)\n\n # print(\"==============sel_completion_texts\", sel_completion_texts)\n print(\"==============file_queries\", file_queries)\n \n file_content_strings = [fetch_file_contents(fq) for fq in file_queries]\n print(\"file content strings\", file_content_strings)\n # test_case_suite = implement_auto_tdd(problem_statement, directory_string)[\"test_code\"]\n test_case_suite = implement_auto_tdd(enriched_context, directory_string)[\"test_code\"]\n print(\"test case suite: \", test_case_suite)\n add_to_history(current_issue, HistoryDict.TEST_CASES, test_case_suite)\n\n \n # patch_completion_texts, patch_strings = get_patch_string(problem_statement, file_content_strings, [test_case_suite])\n # patch_completion_texts, patch_strings = get_patch_string(enriched_context, file_content_strings, [test_case_suite])\n patch_completion_texts, patch_strings = get_patch_string(\n problem_statement=enriched_context,\n file_content_strings=file_content_strings,\n test_case_suite=[test_case_suite],\n repo_archive=repo_archive,\n directory=directory\n )\n \n print(\"patch string: \", patch_strings)\n if patch_strings:\n add_to_history(current_issue, HistoryDict.PATCH_GENERATED, str(patch_strings))\n \n # verif_completion_texts_agg, judgments_agg = get_verification(problem_statement, file_content_strings, patch_strings,\n # directory)\n verif_completion_texts_agg, judgments_agg = get_verification(enriched_context, file_content_strings, patch_strings,directory)\n \n scores, patch_string = choose_patch_string(patch_strings, judgments_agg, directory)\n\n # Store the final patch details if a valid patch was generated\n if patch_string:\n add_to_history(current_issue, HistoryDict.DIFF_PATCH, patch_string)\n \n # Extract file path and modified functions from the patch\n try:\n patch_lines = patch_string.split('\\n')\n for line in patch_lines:\n if line.startswith('+++'):\n file_path = line.replace('+++ b/', '')\n add_to_history(current_issue, HistoryDict.FILE_PATH, file_path)\n break\n \n # Store modified functions if we can extract them\n modified_funcs = []\n for line in patch_lines:\n if line.startswith('+') and 'def ' in line:\n func_name = line.split('def ')[1].split('(')[0].strip()\n modified_funcs.append(func_name)\n if modified_funcs:\n add_to_history(current_issue, HistoryDict.MODIFIED_FUNCTIONS, str(modified_funcs))\n except Exception as e:\n print(f\"Error extracting patch details: {e}\")\n \n if not os.getenv(\"KAGGLE_IS_COMPETITION_RERUN\"):\n data = {\n \"problem_statement\": [problem_statement] * len(file_queries),\n \"selection_completion_text\": sel_completion_texts,\n \"selection_completion_length\": [count_tokens(ct) for ct in sel_completion_texts],\n \"file_query\": file_queries,\n \"file_content_string\": file_content_strings,\n # \"patch_completion_text\": patch_completion_texts,\n \"patch_completion_length\": [count_tokens(ct) for ct in patch_completion_texts],\n \"patch_string\": patch_strings,\n }\n for copy_idx in range(VALIDATION_COPY_COUNT):\n data[f\"verification_completion_text_{copy_idx}\"] = [\n comp_texts[copy_idx] if comp_texts else None for comp_texts in verif_completion_texts_agg\n ]\n data[f\"verification_completion_length_{copy_idx}\"] = [\n count_tokens(comp_texts[copy_idx]) if comp_texts else None for comp_texts in verif_completion_texts_agg\n ]\n data[f\"judgment_{copy_idx}\"] = [\n judgments[copy_idx] if judgments else None for judgments in judgments_agg\n ]\n data[\"judgment_count_true\"] = [judgments.count(True) for judgments in judgments_agg]\n data[\"score\"] = scores\n pd.DataFrame(data).to_csv(f\"{str(int(time.time() - start_time)).zfill(5)}.csv\", index=False)\n return patch_string","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:14:13.687441Z","iopub.execute_input":"2025-03-12T14:14:13.687799Z","iopub.status.idle":"2025-03-12T14:14:13.701136Z","shell.execute_reply.started":"2025-03-12T14:14:13.687773Z","shell.execute_reply":"2025-03-12T14:14:13.700441Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\ndef format_historical_insights(insights: List[Dict]) -> str:\n \"\"\"Format historical insights into a readable string.\"\"\"\n if not insights:\n return \"No historical insights available\"\n \n formatted = []\n for insight in insights:\n section = []\n section.append(f\"File: {insight['file']}\")\n section.append(f\"Function: {insight['function']}\")\n \n if insight['co_evolved_functions']:\n section.append(\"Co-evolved functions:\")\n section.extend(f\" - {func}\" for func in insight['co_evolved_functions'])\n \n if insight['related_files']:\n section.append(\"Related files:\")\n section.extend(f\" - {file}\" for file in insight['related_files'])\n \n if insight['previous_fixes']:\n section.append(\"Previous fix patterns:\")\n for fix in insight['previous_fixes']:\n section.append(\" Before:\")\n section.extend(f\" {line}\" for line in fix['before'].split('\\n'))\n section.append(\" After:\")\n section.extend(f\" {line}\" for line in fix['after'].split('\\n'))\n \n formatted.append('\\n'.join(section))\n \n return '\\n\\n'.join(formatted)\n","metadata":{"id":"qoqXuwDWrnVQ","cellView":"form","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:14:23.149892Z","iopub.execute_input":"2025-03-12T14:14:23.150185Z","iopub.status.idle":"2025-03-12T14:14:23.158186Z","shell.execute_reply.started":"2025-03-12T14:14:23.150163Z","shell.execute_reply":"2025-03-12T14:14:23.15756Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"skip_prediction: bool = False\n\ninitial_predictions_left = 5\npredictions_left = initial_predictions_left\n\n\n# def predict(problem_statement: str, repo_archive: io.BytesIO, pip_packages_archive: io.BytesIO,\n# env_setup_cmds_templates: List[str]) -> Optional[str]:\n# # global skip_prediction\n# # if skip_prediction:\n# # return None\n\n# allowed_time[-1] += 6 * 60\n# if time.time() > allowed_time[-1]:\n# return None\n\n# global predictions_left\n# if predictions_left == 0:\n# return None\n# with open(\"repo_archive.tar\", \"wb\") as f:\n# f.write(repo_archive.read())\n# repo_path: str = REPO_PATH\n# if os.path.exists(repo_path):\n# shutil.rmtree(repo_path)\n# shutil.unpack_archive(\"repo_archive.tar\", extract_dir=repo_path)\n# os.remove(\"repo_archive.tar\")\n# patch_string = process_issue(problem_statement=problem_statement, directory=repo_path)\n# if os.path.exists(repo_path):\n# shutil.rmtree(repo_path)\n# if os.getenv(\"KAGGLE_IS_COMPETITION_RERUN\"):\n# predictions_left -= 1\n# else:\n# predictions_left = 0\n# print(\"submitted patch_string\")\n# print(patch_string)\n# return patch_string\n\ndef predict(problem_statement: str, repo_archive: io.BytesIO, pip_packages_archive: io.BytesIO,\n env_setup_cmds_templates: List[str]) -> Optional[str]:\n allowed_time[-1] += 6 * 60\n if time.time() > allowed_time[-1]:\n return None\n\n # global predictions_left\n # if predictions_left == 0:\n # return None\n\n repo_path: str = REPO_PATH\n if not os.path.exists(repo_path):\n os.makedirs(repo_path)\n\n setup(repo_archive, pip_packages_archive, env_setup_cmds_templates, repo_path)\n\n patch_string = process_issue(problem_statement=problem_statement, directory=repo_path)\n\n if os.path.exists(repo_path):\n shutil.rmtree(repo_path)\n\n if os.getenv(\"KAGGLE_IS_COMPETITION_RERUN\"):\n predictions_left -= 1\n else:\n predictions_left = 0\n\n if patch_string is None:\n return None\n\n return patch_string\n\n\n\n\ndef setup(repo_archive: io.BytesIO, pip_packages_archive: io.BytesIO, env_setup_cmds_templates: list[str],\n repo_path: str) -> None:\n archive_path = \"/tmp/repo_archive.tar\"\n with open(archive_path, \"wb\") as f:\n f.write(repo_archive.read())\n if os.path.exists(repo_path):\n shutil.rmtree(repo_path)\n shutil.unpack_archive(archive_path, extract_dir=repo_path)\n os.remove(archive_path)\n\n pip_archive_dir = \"/tmp/pip_packages_archive.tar\"\n with open(pip_archive_dir, \"wb\") as f:\n f.write(pip_packages_archive.read())\n pip_packages_path = \"/path/to/pip_packages\"\n if os.path.exists(pip_packages_path):\n shutil.rmtree(pip_packages_path)\n shutil.unpack_archive(pip_archive_dir, extract_dir=pip_packages_path)\n os.remove(pip_archive_dir)\n\n env_setup_cmds = [\n cmd.format(pip_packages_path=pip_packages_path)\n for cmd in env_setup_cmds_templates\n ]\n\n subprocess.run(\"\\n\".join(env_setup_cmds), shell=True, executable=\"/bin/bash\", cwd=repo_path)","metadata":{"id":"qoqXuwDWrnVQ","cellView":"form","trusted":true,"execution":{"iopub.status.busy":"2025-03-12T14:14:23.149892Z","iopub.execute_input":"2025-03-12T14:14:23.150185Z","iopub.status.idle":"2025-03-12T14:14:23.158186Z","shell.execute_reply.started":"2025-03-12T14:14:23.150163Z","shell.execute_reply":"2025-03-12T14:14:23.15756Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# # @title Inference Server Setup\n# # =========================\n# def get_number_of_instances(num_instances: int) -> None:\n# global instance_count\n# instance_count = num_instances\ndef get_number_of_instances(num_instances: int) -> None:\n \"\"\"The very first message from the gateway will be the total number of instances to be served.\n You don't need to edit this function.\n \"\"\"\n global instance_count\n instance_count = num_instances\n\n# inference_server = kaggle_evaluation.konwinski_prize_inference_server.KPrizeInferenceServer(get_number_of_instances, predict)\n\n# if os.getenv(\"KAGGLE_IS_COMPETITION_RERUN\"):\n# inference_server.serve()\n# else:\n# inference_server.run_local_gateway(\n# data_paths=(\"/kaggle/input/konwinski-prize/\", \"/kaggle/tmp/konwinski-prize/\"),\n# use_concurrency=True,\n# # )","metadata":{"id":"DooQmQ5MrvR9","cellView":"form","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"skip_prediction = False\ninference_server = (\n kaggle_evaluation.konwinski_prize_inference_server.KPrizeInferenceServer(\n get_number_of_instances, predict\n )\n)\n\nif os.getenv(\"KAGGLE_IS_COMPETITION_RERUN\"):\n inference_server.serve()\nelse:\n inference_server.run_local_gateway(\n data_paths=(\n \"/kaggle/input/konwinski-prize/\", # Path to the entire competition dataset\n \"/kaggle/working/konwinski-prize/\", # Path to a scratch directory for unpacking data.a_zip.\n ), # type: ignore\n use_concurrency=True,\n )\n","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}