SapFix: Automated End-to-End Repair at Scale

Marginean, Alexandru; Bader, Johannes; Chandra, Satish; Harman, Mark; Jia, Yunyi; Mao, Ke; Mols, Alexander; Scott, Andrew

doi:10.1109/icse-seip.2019.00039

Cited by 120 publications

(60 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As long as the bug category and bug location is known, and a training set of fixes is available, Getafix can offer fixes learned from the training set. Case in point, Getafix suggests fixes Ð via SapFix 4 [Marginean et al 2019] Ð for null pointer exceptions detected by Sapienz 5 , an automated testing system for mobile apps.…”

Section: Resultsmentioning

confidence: 99%

Getafix: learning to fix bugs automatically

Bader

Scott

Pradel

et al. 2019

Proc. ACM Program. Lang.

Self Cite

196

121

View full text Add to dashboard Cite

Static analyzers help find bugs early by warning about recurring bug categories. While fixing these bugs still remains a mostly manual task in practice, we observe that fixes for a specific bug category often are repetitive. This paper addresses the problem of automatically fixing instances of common bugs by learning from past fixes. We present Getafix, an approach that produces human-like fixes while being fast enough to suggest fixes in time proportional to the amount of time needed to obtain static analysis results in the first place. Getafix is based on a novel hierarchical clustering algorithm that summarizes fix patterns into a hierarchy ranging from general to specific patterns. Instead of an expensive exploration of a potentially large space of candidate fixes, Getafix uses a simple yet effective ranking technique that uses the context of a code change to select the most appropriate fix for a given bug. Our evaluation applies Getafix to 1,268 bug fixes for six bug categories reported by popular static analyzers for Java, including null dereferences, incorrect API calls, and misuses of particular language constructs. The approach predicts exactly the human-written fix as the topmost suggestion between 12% and 91% of the time, depending on the bug category. The top-5 suggestions contain fixes for 526 of the 1,268 bugs. Moreover, we report on deploying the approach within Facebook, where it contributes to the reliability of software used by billions of people. To the best of our knowledge, Getafix is the first industrially-deployed automated bug-fixing tool that learns fix patterns from past, human-written fixes to produce human-like fixes. CCS Concepts: • Software and its engineering → Software testing and debugging.

show abstract

Section: Resultsmentioning

confidence: 99%

Getafix: learning to fix bugs automatically

Bader

Scott

Pradel

et al. 2019

Proc. ACM Program. Lang.

Self Cite

196

121

View full text Add to dashboard Cite

show abstract

“…Automated program repair, specifically test-suite driven program repair, has gained traction in recent years. This includes a recent use of test-driven automated repair at scale in Facebook [37], reporting positive developer feedback. However, the automatically generated patches can over-fit the test suite T driving the repair, and their behavior on tests outside T is unknown.…”

Section: Resultsmentioning

confidence: 99%

Crash-avoiding program repair

Gao

Mechtaev

Roychoudhury

2019

Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis

View full text Add to dashboard Cite

Existing program repair systems modify a buggy program so that the modified program passes given tests. The repaired program may not satisfy even the most basic notion of correctness, namely crash-freedom. In other words, repair tools might generate patches which over-fit the test data driving the repair, and the automatically repaired programs may even introduce crashes or vulnerabilities. We propose an integrated approach for detecting and discarding crashing patches. Our approach fuses test and patch generation into a single process, in which patches are generated with the objective of passing existing tests, and new tests are generated with the objective of filtering out over-fitted patches by distinguishing candidate patches in terms of behavior. We use crash-freedom as the oracle to discard patch candidates which crash on the new tests. In its core, our approach defines a grey-box fuzzing strategy that gives higher priority to new tests that separate patches behaving equivalently on existing tests. This test generation strategy identifies semantic differences between patch candidates, and reduces over-fitting in program repair. We evaluated our approach on real-world vulnerabilities and open-source subjects from the Google OSS-Fuzz infrastructure. We found that our tool Fix2Fit (implementing patch space directed test generation), produces crash-avoiding patches. While we do not give formal guarantees about crash-freedom, cross-validation with fuzzing tools and their sanitizers provides greater confidence about the crash-freedom of our suggested patches. P into two subsets P crash−free (crash-free plausible patches), and

show abstract

“…It grew out of research prototype and now runs in continuous production at Facebook, testing changes to mobile apps as they are submitted [1]. Some of the crashes it reveals are also automatically fixed [14].…”

Section: Introductionmentioning

confidence: 99%