Augmenting Automated Spectrum Based Fault Localization for Multiple Faults

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Chatterjee, P (Author) Other; José Campos (Author) FEUP; Rui Abreu (Author) FEUP; Roy, S (Author) Other

Conference proceedings International

Title: IJCAI International Joint Conference on Artificial Intelligence

Pages: 3140-3148

32nd International Joint Conference on Artificial Intelligence, IJCAI 2023

Macao, 19 August 2023 through 25 August 2023

Indexing

ISI Web of Knowledge - 0 Citations

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Other information

Authenticus ID: P-00Z-176

DOI: 10.24963/ijcai.2023/350

Abstract (EN): Spectrum-based Fault Localization (SBFL) uses the coverage of test cases and their outcome (pass/fail) to predict the suspiciousness of program components, e.g., lines of code. SBFL is, perhaps, the most successful fault localization technique due to its simplicity and scalability. However, SBFL heuristics do not perform well in scenarios where a program may have multiple faulty components. In this work, we propose a new algorithm that augments previously proposed SBFL heuristics to produce a ranked list where faulty components ranked low by base SBFL metrics are ranked significantly higher. We implement our ideas in a tool, ARTEMIS, that attempts to bubble up faulty components which are ranked lower by base SBFL metrics. We compare our technique to the most popular SBFL metrics and demonstrate statistically significant improvement in the developer effort for fault localization with respect to the basic strategies.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9