Evaluation of Measures for Statistical Fault Localisation and an Optimising Scheme

David Landsberg, Hana Chockler, Daniel Kroening, Matt Lewis

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

19 Citations (Scopus)
222 Downloads (Pure)


Statistical Fault Localisation (SFL) is a widely used method for localizing faults in software. SFL gathers coverage details of passed and failed executions over a faulty program and then uses a measure to assign a degree of suspiciousness to each of a chosen set of program entities (statements, predicates, etc.) in that program. The program entities are then inspected by the engineer in descending order of suspiciousness until the bug is found. The effectiveness of this process relies on the quality of the suspiciousness measure. In this paper, we compare 157 measures, 95 of which are new to SFL and borrowed from other branches of science and philosophy. We also present a new measure optimiser Lex g , which optimises a given measure g according to a criterion of single bug optimality. An experimental comparison on benchmarks from the Software-artifact Infrastructure Repository (SIR) indicates that many of the new measures perform competitively with the established ones. Furthermore, the large-scale comparison reveals that the new measures Lex Ochiai and Pattern-Similarity perform best overall.
Original languageEnglish
Title of host publicationFundamental Approaches to Software Engineering
Subtitle of host publication18th International Conference, FASE 2015, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2015, London, UK, April 11-18, 2015, Proceedings
EditorsAlexander Egyed, Ina Schaefer
Place of PublicationLondon, UK
PublisherSpringer International Publishing
Number of pages15
ISBN (Print)9783662466742
Publication statusPublished - 2015

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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