Effective fault localization using probabilistic and grouping approach

Saksham Sahai Srivastava (), Arpita Dutta () and Rajib Mall ()
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Saksham Sahai Srivastava: University of Colorado Boulder
Arpita Dutta: National University of Singapore
Rajib Mall: Indian Institute of Technology Kharagpur

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 9, No 24, 4616-4635

Abstract: Abstract Fault localization (FL) is the key activity while debugging a program. Any improvement to this activity leads to significant improvement in total software development cost. In the paper, we present a conditional probability statistics based fault localization technique that derives the association between statement coverage information and test case execution result. This association with the failed test case result shows the fault containing probability of that specific statement. Subsequently, we use a grouping method to refine the obtained statement ranking sequence for better fault localization. We named our proposed FL technique as CGFL, it is an abbreviation of Conditional probability and Grouping based Fault Localization. We evaluated the effectiveness of the proposed method over eleven open-source data sets from Defects4j and SIR repositories. Our obtained results show that on average, the proposed CGFL method is 24.56% more effective than contemporary FL techniques namely D $$^*$$ ∗ , Tarantula, Ochiai, Crosstab, BPNN, RBFNN, DNN, and CNN.

Keywords: Fault localization; Program analysis; Debugging; Conditional probability; Grouping (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02479-5

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