Paper Info
Title
Quality of Automated Program Repair on Real-World Defects
Abstract
Automated program repair is a promising approach to reducing the costs of manual debugging and increasing software quality. However, recent studies have shown that automated program repair techniques can be prone to producing patches of low quality, overfitting to the set of tests provided to the repair technique, and failing to generalize to the intended specification. This paper rigorously explores this phenomenon on real-world Java programs, analyzing the effectiveness of four well-known repair techniques, GenProg, Par, SimFix, and TrpAutoRepair, on defects made by the projects’ developers during their regular development process. We find that: (1) When applied to real-world Java code, automated program repair techniques produce patches for between 10.6 and 19.0 percent of the defects, which is less frequent than when applied to C code. (2) The produced patches often overfit to the provided test suite, with only between 13.8 and 46.1 percent of the patches passing an independent set of tests. (3) Test suite size has an extremely small but significant effect on the quality of the patches, with larger test suites producing higher-quality patches, though, surprisingly, higher-coverage test suites correlate with lower-quality patches. (4) The number of tests that a buggy program fails has a small but statistically significant positive effect on the quality of the produced patches. (5) Test suite provenance, whether the test suite is written by a human or automatically generated, has a significant effect on the quality of the patches, with developer-written tests typically producing higher-quality patches. And (6) the patches exhibit insufficient diversity to improve quality through some method of combining multiple patches. We develop JaRFly, an open-source framework for implementing techniques for automatic search-based improvement of Java programs. Our study uses JaRFly to faithfully reimplement GenProg and TrpAutoRepair to work on Java code, and makes the first public release of an implementation of Par. Unlike prior work, our study carefully controls for confounding factors and produces a methodology, as well as a dataset of automatically-generated test suites, for objectively evaluating the quality of Java repair techniques on real-world defects.
Year
DOI
Venue
2022
10.1109/TSE.2020.2998785
IEEE Transactions on Software Engineering
Keywords
DocType
Volume
Automated program repair,patch quality,objective quality measure,Java,GenProg,Par,TrpAutoRepair,Defects4J
Journal
48
Issue
ISSN
Citations 
2
0098-5589
2
PageRank 
References 
Authors
0.36
87
5
Name
Order
Citations
PageRank
Manish Motwani120.36
Mauricio Soto220.36
Yuriy Brun3221691.89
Rene Just495833.77
Claire Le Goues5176668.79