Faeze Aghazade-Par; Mojtaba Vahidi-Asl
Abstract
Spectrum-Based Fault Localization (SBFL) techniques are widely used and studied. These techniques are straightforward, low-cost, and fast in comparison to other fault localization techniques, ...
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Spectrum-Based Fault Localization (SBFL) techniques are widely used and studied. These techniques are straightforward, low-cost, and fast in comparison to other fault localization techniques, such as Mutation-Based and Learning-Based ones. However, the accuracy of these techniques is always controversial. The main criticism of these techniques refers to the mere use of coverage and spectra information. Hence, this study seeks to improve SBFL techniques by enhancing their accuracy while maintaining simplicity, aiming to make them applicable for regression faults. To achieve this, the first step involves integrating SBFL techniques with two features: (1) Change and (2) Test Case Weight, which utilizes the same coverage and spectrum data in a novel way. Additionally, the study examines the impact of incorporating Data and Control Dependency into the formula for fault localization accuracy. It also considers the suspiciousness scores generated by SBFL formulae as a feature. Three SBFL techniques—Tarantula, Ochiai, and Jaccard—are applied in this study both as a feature and as a baseline for result comparison. The findings reveal that combining SBFL suspiciousness scores with Change and Test Case Weight significantly enhances performance by identifying at least 27% more faults at the Top-3 ranking. Furthermore, integrating these features with Data Dependency leads to even greater improvements, locating minimum 45% more faults at the Top-3 ranking compared to aforementioned SBFL formulae. Overall, this study emphasizes the limitations of existing SBFL formulae while highlighting the advantages of augmenting SBFL with additional features and repurposing spectral information to achieve greater accuracy in fault localization.
