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Effective Spectrum Based Fault Localization Using Contextual Based Importance Weight

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Quality of Information and Communications Technology (QUATIC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1621))

Abstract

In Spectrum-Based Fault Localization (SBFL), a suspicion score for each program element (e.g., statement, method, or class) is calculated by using a risk evaluation formula based on tests coverage and their results. The elements are then ranked from most suspicious to least suspicious based on their scores. The elements with the highest scores are thought to be the most faulty. The final ranking list of program elements helps testers during the debugging process when seeking the source of a fault in the program under test. In this paper, we present an approach that gives more importance to program elements that are executed by more failed test cases and appear in different contexts of method calls (both as callees and as callers) in these tests compared to other elements. In essence, we are emphasizing the failing test cases factor because there are comparably much less failing tests than passing ones. We multiply each element’s suspicion score obtained by a SBFL formula by this importance weight, which is the ratio of covering failing tests over all failing tests combined with the so-called method calls frequency. The proposed approach can be applied to SBFL formulas without modifying their structures. The experimental results of our study show that our approach achieved a better performance in terms of average ranking compared to the underlying SBFL formulas and comparable approaches. It also improved the Top-N categories and increased the number of cases in which the faulty method became the top-ranked element.

The research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program (RRF-2.3.1-21-2022-00004) and the project no. TKP2021-NVA-09 which was implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme.

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Notes

  1. 1.

    https://github.com/rjust/defects4j/tree/v1.5.0.

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Authors and Affiliations

  1. Department of Software Engineering, University of Szeged, Szeged, Hungary

    Qusay Idrees Sarhan & Árpád Beszédes

  2. Department of Computer Science, University of Duhok, Duhok, Iraq

    Qusay Idrees Sarhan

Authors
  1. Qusay Idrees Sarhan
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  2. Árpád Beszédes
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Corresponding author

Correspondence to Qusay Idrees Sarhan .

Editor information

Editors and Affiliations

  1. University of Malaga, Málaga, Spain

    Antonio Vallecillo

  2. Leiden University, Leiden, The Netherlands

    Joost Visser

  3. University of Castila-La Mancha, Ciudad Real, Spain

    Ricardo Pérez-Castillo

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Sarhan, Q.I., Beszédes, Á. (2022). Effective Spectrum Based Fault Localization Using Contextual Based Importance Weight. In: Vallecillo, A., Visser, J., Pérez-Castillo, R. (eds) Quality of Information and Communications Technology. QUATIC 2022. Communications in Computer and Information Science, vol 1621. Springer, Cham. https://doi.org/10.1007/978-3-031-14179-9_7

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  • DOI: https://doi.org/10.1007/978-3-031-14179-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14178-2

  • Online ISBN: 978-3-031-14179-9

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