Spectrum-based multi-fault localization using Chaotic Genetic Algorithm

Debolina Ghosh

doi:10.1016/j.infsof.2021.106512

2021-05-01

PRE

Debolina Ghosh *

Jagannath Singh

DOI：10.1016/j.infsof.2021.106512

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摘要

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Context: In the field of software engineering, the most complex and time consuming activity is fault-finding. Due to increasing size and complexity of software, there is a necessity of automated fault detection tool which can detect fault with minimal human intervention. A programmer spends a lot of time and effort on software fault localization. Various Spectrum Based Fault Localization (SBFL) techniques have already been developed to automate the fault localization in single-fault software. But, there is a scarcity of fault localization technique for multi-fault software. In our study, we have found that pure SBFL is not always sufficient for effective fault localization in multi-fault programs. Objective: To address the above challenge, we propose an automated framework using Chaos-based Genetic Algorithm for Multi-fault Localization (CGAML) based on SBFL technique. Methods: Traditional Genetic Algorithm (GA) sometimes stuck in local optima, and it takes more time to converge. Different chaos mapping functions have been applied to GA for better performance. We have used logistic mapping function to achieve chaotic sequence. The proposed technique CGAML first calculates the suspiciousness score for each program statement and then assigns ranks according to that score. The statements having smaller rank means there is a high probability of the statements to be faulty. Results: Five open-source benchmark programs are tested to evaluate the efficiency of CGAML technique. The experimental results show CGAML gives better results for both single-fault and multi-fault programs in comparison with existing spectrum-based fault localization techniques. Conclusion: EXAM metric is used to compare the performance of our proposed technique with other existing techniques. Smaller EXAM score denotes the higher accuracy of the technique. The proposed framework generates smaller EXAM score in comparison with other existing techniques. We found that, overall CGAML works on an average 8.5% better than GA for both single-fault and multi-fault software.

Keyword:

Debugging

Software testing

Spectrum-based debugging

Genetic Algorithm

Chaotic GA

Fault localization