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Cited by 328Software Testing and Debugging TechniquesSoftware Engineering ResearchEvolutionary Algorithms and Applications

The DOI matches its Crossref record; 66 of the 68 references it cites were found in a registry or an index.

Is the cure worse than the disease? overfitting in automated program repair

Smith, Edward K., Barr, Earl T., Le Goues, Claire, Brun, Yuriy·2015·Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering
DOI 10.1145/2786805.2786825
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Abstract

Automated program repair has shown promise for reducing the significant manual effort debugging requires. This paper addresses a deficit of earlier evaluations of automated repair techniques caused by repairing programs and evaluating generated patches' correctness using the same set of tests. Since tests are an imperfect metric of program correctness, evaluations of this type do not discriminate between correct patches and patches that overfit the available tests and break untested but desired functionality. This paper evaluates two well-studied repair tools, GenProg and TrpAutoRepair, on a publicly available benchmark of bugs, each with a human-written patch. By evaluating patches using tests independent from those used during repair, we find that the tools are unlikely to improve the proportion of independent tests passed, and that the quality of the patches is proportional to the coverage of the test suite used during repair. For programs that pass most tests, the tools are as likely to break tests as to fix them. However, novice developers also overfit, and automated repair performs no worse than these developers. In addition to overfitting, we measure the effects of test suite coverage, test suite provenance, and starting program quality, as well as the difference in quality between novice-developer-written and tool-generated patches when quality is assessed with a test suite independent from the one used for patch generation.

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Research works printed but not linked: 6

  1. L. Chen and A. Avižienis . N-version programming: A faulttolerance approach to reliability of software operation . In IEEE International Symposium on Fault-Tolerant Computing (FTCS) , pages 3– 9 , 1978 . L. Chen and A. Avižienis. N-version programming: A faulttolerance approach to reliability of software operation. In IEEE International Symposium on Fault-Tolerant Computing (FTCS), pages 3–9, 1978.
  2. 10.5555/2486788.2486893 · DOI
  3. 10.5555/2337223.2337225 · DOI
  4. 10.5555/2818754.2818811 · DOI
  5. 10.5555/541177 · DOI
  6. Research Triangle Institute . The economic impacts of inadequate infrastructure for software testing. NIST Planning Report 02-3, May 2002 . Research Triangle Institute. The economic impacts of inadequate infrastructure for software testing. NIST Planning Report 02-3, May 2002.

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Metadata

Authors
Smith, E. K., Barr, E. T., Le Goues, C., & Brun, Y.
Affiliations
  • University of Massachusetts Amherst (US)
  • University College London (GB)
  • Carnegie Mellon University, USA
  • University of Massachusetts, USA
ORCID
Venue
Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering
Publisher
ACM
Published
2015-08-30
Pages
532-543
DOI
10.1145/2786805.2786825
OpenAlex
W2061575154
Cited by
323
References
68
Citations by year
2026: 18 · 2025: 25 · 2024: 33 · 2023: 22 · 2022: 29 · 2021: 26 · 2020: 37 · 2019: 40 · 2018: 43 · 2017: 27 · 2016: 19 · 2015: 4
FWCI
23.83
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (publishedVersion)
Funders
  • National Science Foundation (CCF-1446683, CCF-1446966, CCF-1453508)
  • Microsoft Research (Software Engineering Innovation Foundation Award)
Topics
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
Keywords
automated program repair, overfitting, patch evaluation, test suite coverage, novice programmers, program correctness

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