Multiple bug spectral fault localization using genetic programming

Lee Naish, Neelofar, Kotagiri Ramamohanarao

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

12 Citations (Scopus)


Debugging is crucial for producing reliable software. One of the effective bug localization techniques is Spectral-Based Fault Localization (SBFL). It locates a buggy statement by applying an evaluation metric to program spectra and ranking program components on the basis of the score it computes. Recently, genetic programming has been proposed as a way to find good metrics. We have found that the huge search space for metrics can cause this approach to be slow and unreliable, even for relatively simple data sets. Here we propose a restricted class of “hyperbolic” metrics, with a small number of numeric parameters. This class of functions is based on past theoretical and empirical results. We show that genetic programming can reliably discover effective metrics over a wide range of data sets of program spectra. We evaluate the performance for both real programs and model programs with single bugs, multiple bugs, “deterministic” bugs and nondeterministic bugs.

Original languageEnglish
Title of host publicationProceedings - 2015 24th Australasian Software Engineering Conference, ASWEC 2015
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781467393904
Publication statusPublished - 2015
Externally publishedYes
EventAustralasian Software Engineering Conference 2015 - Adelaide, Australia
Duration: 28 Sept 20151 Oct 2015
Conference number: 24th (Proceedings)


ConferenceAustralasian Software Engineering Conference 2015
Abbreviated titleASWEC 2015
Internet address

Cite this