Bug detection and repair benchmark with ~2.4M Java methods mined from GitHub commits labeled as bug fixes. Used widely to evaluate LLM bug detection capabilities. Primary metric is Accuracy (correct bug classification).
6 results indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.
| # | Model | Org | Submitted | Paper / code | accuracy |
|---|---|---|---|---|---|
| 01 | GPT-4oAPI | OpenAI | Mar 2026 | arxiv | 78.60 |
| 02 | Qwen2.5-Coder 32BOSS | Alibaba | Sep 2024 | Qwen2.5-Coder Technical Report · code | 76.80 |
| 03 | DeepSeek-Coder-V2-InstructOSS | DeepSeek | Jun 2024 | DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source… · code | 75.30 |
| 04 | CodeT5+OSS | Salesforce | May 2023 | CodeT5+: Open Code Large Language Models for Code Unders… · code | 68.20 |
| 05 | UniXcoderOSS | Microsoft | Mar 2022 | UniXcoder: Unified Cross-Modal Pre-Training for Code Rep… · code | 66.40 |
| 06 | CodeBERTOSS | Microsoft | Feb 2020 | CodeBERT: A Pre-Trained Model for Programming and Natura… · code | 62.50 |
Each row below marks a model that broke the previous record on accuracy. Intermediate submissions are kept in the leaderboard above; only SOTA-setting entries are re-listed here.
Higher scores win. Each subsequent entry improved upon the previous best.
Every paper below corresponds to at least one row in the leaderboard above. Click through for the arXiv preprint and, when available, the reference implementation.
Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.