Mutation-Based Graph Inference for Fault Localization - Archive ouverte HAL Access content directly
Conference Papers Year :

Mutation-Based Graph Inference for Fault Localization

(1, 2, 3, 4) , (2, 3) , (1, 4)
1
2
3
4

Abstract

We present a new fault localization algorithm, called Vautrin, built on an approximation of causality based on call graphs. The approximation of causality is done using software mutants. The key idea is that if a mutant is killed by a test, certain call graph edges within a path between the mutation point and the failing test are likely causal. We evaluate our approach on the fault localization benchmark by Steimann et al. totaling 5,836 faults. The causal graphs are extracted from 88,732 nodes connected by 119,531 edges. Vautrin improves the fault localization effectiveness for all subjects of the benchmark. Considering the wasted effort at the method level, a classical fault localization evaluation metric, the improvement ranges from 3% to 55%, with an average improvement of 14%.
Fichier principal
Vignette du fichier
scam16.pdf (241.05 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01350515 , version 1 (12-08-2016)

Identifiers

Cite

Vincenzo Musco, Martin Monperrus, Philippe Preux. Mutation-Based Graph Inference for Fault Localization. International Working Conference on Source Code Analysis and Manipulation, Oct 2016, Raleigh, United States. ⟨10.1109/SCAM.2016.24⟩. ⟨hal-01350515⟩
222 View
950 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More