Abstract
The primary goal of Automated Program Repair (APR) is to automatically fix buggy software, to reduce the manual bug-fix burden that presently rests on human developers. Existing APR techniques can be generally divided into two families: semantics- vs. heuristics-based. Semantics-based APR uses symbolic execution and test suites to extract semantic constraints, and uses program synthesis to synthesize repairs that satisfy the extracted constraints. Heuristic-based APR generates large populations of repair candidates via source manipulation, and searches for the best among them. Both families largely rely on a primary assumption that a program is correctly patched if the generated patch leads the program to pass all provided test cases. Patch correctness is thus an especially pressing concern. A repair technique may generate overfitting patches, which lead a program to pass all existing test cases, but fails to generalize beyond them. In this work, we revisit the overfitting problem with a focus on semantics-based APR techniques, complementing previous studies of the overfitting problem in heuristics-based APR. We perform our study using IntroClass and Codeflaws benchmarks, two datasets well-suited for assessing repair quality, to systematically characterize and understand the nature of overfitting in semantics-based APR. We find that similar to heuristics-based APR, overfitting also occurs in semantics-based APR in various different ways.
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Notes
Angelix can target multiple expressions at once; we explain the process with respect to a single buggy expression for clarity, but the technique generalizes naturally.
We use the words “repair” and “patch” interchangeably.
We discuss the real-world bugs we describe qualitatively in Section 3.6.
Recall the explanation in Section 2.3 on the number of tests used for specification inference.
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Acknowledgements
We thank the authors of Angelix for making the tool publicly available. We also thank the authors of CVC4 and Enumerative synthesis engines for making these engines accessible.
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Communicated by: Martin Monperrus and Westley Weimer
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Le, X.B.D., Thung, F., Lo, D. et al. Overfitting in semantics-based automated program repair. Empir Software Eng 23, 3007–3033 (2018). https://doi.org/10.1007/s10664-017-9577-2
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DOI: https://doi.org/10.1007/s10664-017-9577-2