Fusion fault localizers

Lucia, David Lo, Xin Xia

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

25 Citations (Scopus)

Abstract

Many spectrum-based fault localization techniques have been proposed to measure how likely each program element is the root cause of a program failure. For various bugs, the best technique to localize the bugs may differ due to the characteristics of the buggy programs and their program spectra. In this paper, we leverage the diversity of existing spectrum-based fault localization techniques to better localize bugs using data fusion methods. Our proposed approach consists of three steps: score normalization, technique selection, and data fusion. We investigate two score normalization methods, two technique selection methods, and five data fusion methods resulting in twenty variants of Fusion Localizer. Our approach is bug specific in which the set of techniques to be fused are adaptively selected for each buggy program based on its spectra. Also, it requires no training data, i.e., execution traces of the past buggy programs. We evaluate our approach on a common benchmark dataset and a dataset consisting of real bugs from three medium to large programs. Our evaluation demonstrates that our approach can significantly improve the effectiveness of existing state-of-the-art fault localization techniques. Compared to these state-of-the-art techniques, the best variants of Fusion Localizer can statistically significantly reduce the amount of code to be inspected to find all bugs. Our best variants can increase the proportion of bugs localized when developers only inspect the top 10% most suspicious program elements by more than 10% and increase the number of bugs that can be successfully localized when developers only inspect up to 10 program blocks by more than 20%.

Original languageEnglish
Title of host publicationASE'14
Subtitle of host publicationProceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering
EditorsMarsha Chechik , Paul Grünbacher
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages127-138
Number of pages12
ISBN (Electronic)9781450330138
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventAutomated Software Engineering Conference 2014 - Vasteras, Sweden
Duration: 15 Sep 201419 Sep 2014
Conference number: 29th
http://ase2014.org/ (Conference website)
https://dl.acm.org/doi/proceedings/10.1145/2642937 (Proceedings)

Conference

ConferenceAutomated Software Engineering Conference 2014
Abbreviated titleASE 2014
CountrySweden
CityVasteras
Period15/09/1419/09/14
Internet address

Keywords

  • Data fusion
  • Fault localization

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