Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2018

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.

Keywords

Automated program repair, Program synthesis, Symbolic execution, Patch overfitting

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Empirical Software Engineering

Volume

23

Issue

5

First Page

3007

Last Page

3033

ISSN

1382-3256

Identifier

10.1007/s10664-017-9577-2

Publisher

Springer Verlag (Germany)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s10664-017-9577-2

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