Provide complete decision chain data from "problem to fix" for code large model and AI programming assistant, speed up software project AI down against SWE-Benchmark Land
对标SWE-Benchmark国际标准 · 专注代码轨迹语料采集与标注
Large models of software engineering training lack data on decision-making in the real world and model capabilities are difficult to break
Existing code models are good at code generation, but real software engineering tasks such as Bug positioning, commissioning and repair are weak to solve complex engineering problems。
Real software engineering mission training data are scarce, models perform well in synthetic data, and performance is significantly reduced when moving to real projects。
Existing code data sets contain only the code itself, lack a complete decision chain from problem understanding to code modification, and models cannot learn engineering thinking。
The code development scene of multiAgent collaboration is becoming more common, but collaborative process trajectories data are not systematically collected to train collaborative code intelligence Body。
Capacity to capture and mark code decision tracks that cover the entire life cycle of software engineering
GitHub Issue到PR全链条采集
Code Navigation and Positioning Decision Record
Debug an iterative process full chain record
多Agent协作轨迹采集
Code Review Track Mark
Bug fixes the validation log.
Test case to generate track indications
Reshaping decision-making labels
Benchmarking international standard data sets
| Parameter Item | Specifications |
|---|---|
| Data Format | Problem description + code navigation + modification + certification of full chain |
| 对标标准 | SWE-Bench / SWE-Bench-Verified |
| Language Overwrite | Python/Java/JavaScript/Go/Rust/C++等 |
| 采集方式 | 真实GitHub项目 Issue→PR |
| Annotation Dimensions | Decision chain/code change/test validation/Agent collaboration |
| Data size | 持续扩充中 |
| Quality Assurance | Level 3 label + consistency test |
SFT training using code trajectories to enhance model performance on real engineering tasks。
Optimization of Copilot, from completion code to real engineering issues。
Training in auto-Bug restoration of intelligence and automation of the entire process from positioning to restoration。
Trained multiAgent code collaboration system to simulate real team development processes。
Reviewing smart bodies based on trajectories training codes to improve R & D efficiency and quality。
"Rang Hui's code trajectories are making our SWE-Bench pass rate significantly higher."
"With complete data on the decision chain, our A.I.A. is no longer just a complete code, but a real solution to the engineering problem."
"Track data are of high quality and the decision chain is far more complete than the same product."
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