yuntong

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Comment · 2 years ago

The developer written testcases are provided in SWE-bench-lite so those could be used to check the generated patches.

The auto-generated patches are to reduce the effort of resolving issues. In practice, they should be reviewed and verified by human developers before they are integrated.

Comment · 2 years ago

The previous comment was to describe the experiment settings. AutoCodeRover currently generates patches. Auto-generating high quality tests can be a parallel effort and another direction to explore. These efforts can eventually be used together.

Comment · 2 years ago

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

Comment · 2 years ago

The problem statement of each issue is included in each result folder as `problem_statement.txt` (such as: https://github.com/nus-apr/auto-code-rover/blob/main/results...).

The developer patch for each issue is similarly included as `developer_patch.diff`.

Comment · 2 years ago

AutoCodeRover does not require or assume a unit test to generate patches. The results discussed in Section 6.1 of the ArXiv paper are generated without any unit test. The unit tests are used by SWE-bench, when evaluating the correctness of AutoCodeRover-generated patches.

That being said, when some unit tests are available (either written by developers or with assistance from other tools), AutoCodeRover can make use of them to perform some analysis like Spectrum-based Fault Localization (SBFL). This kind of analysis output can help the agent in pinpointing locations to fix. (Please see Section 6.2 for the analysis on SBFL.)