Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2017

Abstract

A notable class of techniques for automatic program repair is known as semantics-based. Such techniques, e.g., Angelix, infer semantic specifications via symbolic execution, and then use program synthesis to construct new code that satisfies those inferred specifications. However, the obtained specifications are naturally incomplete, leaving the synthesis engine with a difficult task of synthesizing a general solution from a sparse space of many possible solutions that are consistent with the provided specifications but that do not necessarily generalize. We present S3, a new repair synthesis engine that leverages programming-by-examples methodology to synthesize high-quality bug repairs. The novelty in S3 that allows it to tackle the sparse search space to create more general repairs is three-fold: (1) A systematic way to customize and constrain the syntactic search space via a domain-specific language, (2) An efficient enumeration- based search strategy over the constrained search space, and (3) A number of ranking features based on measures of the syntactic and semantic distances between candidate solutions and the original buggy program. We compare S3’s repair effectiveness with state-of-the-art synthesis engines Angelix, Enumerative, and CVC4. S3 can successfully and correctly fix at least three times more bugs than the best baseline on datasets of 52 bugs in small programs, and 100 bugs in real-world large programs.

Keywords

Inductive synthesis, Program repair, Programming by examples, Symbolic execution

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ESEC/FSE 2017: Proceedings of the 11th Joint Meeting on European Software Engineering Conference and ACM SIGSOFT Symposium on Foundations of Software Engineering, Paderborn, Germany, September 4-8

First Page

593

Last Page

604

ISBN

9781450351058

Identifier

10.1145/3106237.3106309

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3106237.3106309

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