Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2016

Abstract

Automatic Program Repair (APR) is an emerging and rapidly growing research area, with many techniques proposed to repair defective software. One notable state-of-the-art line of APR approaches is known as semantics-based techniques, e.g., Angelix, which extract semantics constraints, i.e., specifications, via symbolic execution and test suites, and then generate repairs conforming to these constraints using program synthesis. The repair capability of such approaches-expressive power, output quality, and scalability-naturally depends on the underlying synthesis technique. However, despite recent advances in program synthesis, not much attention has been paid to assess, compare, or leverage the variety of available synthesis engine capabilities in an APR context. In this paper, we empirically compare the effectiveness of different synthesis engines for program repair. We do this by implementing a framework on top of the latest semantics-based APR technique, Angelix, that allows us to use different such engines. For this preliminary study, we use a subset of bugs in the IntroClass benchmark, a dataset of many small programs recently proposed for use in evaluating APR techniques, with a focus on assessing output quality. Our initial findings suggest that different synthesis engines have their own strengths and weaknesses, and future work on semantics-based APR should explore innovative ways to exploit and combine multiple synthesis engines.

Keywords

Empirical Study, Automated Program Repair, Program Synthesis Engines, computer bugs, benchmark testing

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2016 IEEE International Conference on Software Maintenance and Evolution: ICSME 2016: 2-10 October 2016, Raleigh: Proceedings

ISBN

9781509038060

Identifier

10.1109/ICSME.2016.68

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://doi.org/10.1109/ICSME.2016.68

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