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Evolving Human Competitive Spectra-Based Fault Localisation Techniques

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Search Based Software Engineering (SSBSE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7515))

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Abstract

Spectra-Based Fault Localisation (SBFL) aims to assist debugging by applying risk evaluation formulæ (sometimes called suspiciousness metrics) to program spectra and ranking statements according to the predicted risk. Designing a risk evaluation formula is often an intuitive process done by human software engineer. This paper presents a Genetic Programming (GP) approach for evolving risk assessment formulæ. The empirical evaluation using 92 faults from four Unix utilities produces promising results. Equations evolved by Genetic Programming can consistently outperform many of the human-designed formulæ, such as Tarantula, Ochiai, Jaccard, Ample, and Wong1/2, up to 6 times. More importantly, they can perform equally as well as Op2, which was recently proved to be optimal against If-Then-Else-2 (ITE2) structure, or even outperform it against other program structures.

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Author information

Authors and Affiliations

  1. University College London, UK

    Shin Yoo

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  1. Shin Yoo
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Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Sheffield, Regent Court, 211 Portobello, S1 4DP, Sheffield, UK

    Gordon Fraser

  2. Department of Research and Graduate Studies, State University of Ceara, Av. Parajana 1700, 60714-903, Fortaleza, CE, Brazil

    Jerffeson Teixeira de Souza

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Yoo, S. (2012). Evolving Human Competitive Spectra-Based Fault Localisation Techniques. In: Fraser, G., Teixeira de Souza, J. (eds) Search Based Software Engineering. SSBSE 2012. Lecture Notes in Computer Science, vol 7515. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33119-0_18

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  • DOI: https://doi.org/10.1007/978-3-642-33119-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33118-3

  • Online ISBN: 978-3-642-33119-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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