code-repair

Official repository for the paper "LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code"

Evidence-grounded AI agent for Java code repair with LLM-guided patches and human-in-the-loop review.

Exploring and improving the quality of ChatGPT-generated code for LeetCode programming tasks.

基于 AI Agent 服务自动化修复系统：Agent 自动读取错误日志，定位 Bug，生成补丁，运行测试，提交 PR，并通知开发者 Review。AI-powered auto-fix agent for web services: analyzes logs, patches code, runs tests, and creates pull requests automatically.

Repository-level automated code repair agent using SWE-Bench dataset

Trusted autonomy T&E runtime that links mission needs, hazards, scenarios, telemetry, evidence, verification reports, and hash-chained ledgers so AI/autonomous decisions can be reviewed instead of merely trusted.

A reliability layer for AI-built systems: detect failures (tests or runtime drift), reproduce, repair one ticket at a time behind an approval gate, and prove the fix. The safety boundaries most AI agents skip.

Multilingual software-engineering benchmark with pinned Docker environments & reproducible agent trajectories · 多语言软件工程评测基准

Gymnasium RL environment for training LLM agents to autonomously debug and fix Python code with secure sandboxing and test-driven feedback.

A minimal lab for improving programs, agents and model parameters. Real demos, frozen evaluations, inspectable evidence. Zero runtime dependencies.

🦑 CT 11 — Secure Self-Learning Repair Agent. Droste Fusion. 4 Engines. Reflection Engine. 9/10 Benchmark.

Run broken Python code → it fixes itself. Local-first Python runtime repair.

Enforce authority before AI agents change code. Source-available control plane for federated workload identity, delegated least privilege, pre-tool MCP/API enforcement, evidence-gated human approval, revocation, revision-bound verification, and cryptographically chained receipts. AI proposes. Humans decide.

PyPatch— OpenEnv RL environment where AI agents debug & fix buggy Python code across 3 difficulty levels.

When Free Executors Cost More: The Free-Executor Paradox in Iterative LLM Code-Repair Loops (paper + reproducibility kit)

Execution-grounded reinforcement learning for software repair on SWE-bench

5-agent LangGraph system that autonomously resolves GitHub issues, benchmarked across single-agent baseline, paid API, and open-weight HPC configurations on SWE-bench Lite

Self-healing Python developer tool — uses AST surgery to extract and LLM-repair the exact failing function, then verifies in a closed loop.

Self-healing code reasoning engine. Detective → QA → Patcher closed loop on SWE-bench, with an RL layer that turns every reasoning trace into DPO training data.