SEAug 28, 2018

CFAAR: Control Flow Alteration to Assist Repair

arXiv:1808.09229v12 citations
Originality Incremental advance
AI Analysis

This addresses program repair for developers, but it is incremental as it builds on existing predicate-based methods.

The paper tackles the problem of automated program repair by introducing CFAAR, a technique that alters control flow through predicate negation to fix defects, achieving plausible patches for 41 out of 149 defects with 12 believed correct.

We present CFAAR, a program repair assistance technique that operates by selectively altering the outcome of suspicious predicates in order to yield expected behavior. CFAAR is applicable to defects that are repairable by negating predicates under specific conditions. CFAAR proceeds as follows: 1) it identifies predicates such that negating them at given instances would make the failing tests exhibit correct behavior; 2) for each candidate predicate, it uses the program state information to build a classifier that dictates when the predicate should be negated; 3) for each classifier, it leverages a Decision Tree to synthesize a patch to be presented to the developer. We evaluated our toolset using 149 defects from the IntroClass and Siemens benchmarks. CFAAR identified 91 potential candidate defects and generated plausible patches for 41 of them. Twelve of the patches are believed to be correct, whereas the rest provide repair assistance to the developer.

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