MintHint: Automated Synthesis of Repair Hints

TL;DR

MintHint is a novel program repair technique that generates helpful repair hints using statistical analysis and pattern matching, significantly improving developer productivity and effective even with incomplete repair information.

Contribution

MintHint introduces a new approach for generating repair hints through statistical correlation and pattern matching, differing from fully automated repair methods.

Findings

Significantly improves developer productivity with repair hints.

Performs well even with incomplete or imprecise repair information.

Effective across various common faults in real-world programs.

Abstract

Being able to automatically repair programs is an extremely challenging task. In this paper, we present MintHint, a novel technique for program repair that is a departure from most of today's approaches. Instead of trying to fully automate program repair, which is often an unachievable goal, MintHint performs statistical correlation analysis to identify expressions that are likely to occur in the repaired code and generates, using pattern-matching based synthesis, repair hints from these expressions. Intuitively, these hints suggest how to rectify a faulty statement and help developers find a complete, actual repair. MintHint can address a variety of common faults, including incorrect, spurious, and missing expressions. We present a user study that shows that developers' productivity can improve manyfold with the use of repair hints generated by MintHint -- compared to having only traditional fault localization information. We also apply MintHint to several faults of a widely used Unix utility program to further assess the effectiveness of the approach. Our results show that MintHint performs well even in situations where (1) the repair space searched does not contain the exact repair, and (2) the operational specification obtained from the test cases for repair is incomplete or even imprecise.