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Leveraging Stack Traces for Spectrum-based Fault Localization in the Absence of Failing Tests

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Leveraging Stack Traces for Spectrum-based Fault Localization in the Absence of Failing Tests

Barreto Simedo Pacheco, Lorena (2024) Leveraging Stack Traces for Spectrum-based Fault Localization in the Absence of Failing Tests. Masters thesis, Concordia University.

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Abstract

Bug fixing is a crucial task in software maintenance to hold user trust. Although various automated fault localization techniques exist, they often require specific conditions to be effective. For example, Spectrum-Based Fault Localization (SBFL) techniques need at least one failing test to identify bugs, which may not always be available. Bug reports, particularly those with stack traces, provide detailed information on system execution failures and are invaluable for developers. This study focuses on utilizing stack traces from crash reports as fault-triggering tests for SBFL. Our findings indicate that only 3.33% of bugs have fault-triggering tests, limiting traditional SBFL efficiency. However, 98.3% of bugfix intentions align directly with exceptions in stack traces, and 78.3% of buggy methods are reachable within an average of 0.34 method calls, proving stack traces as a reliable source for locating bugs. We introduce a new approach, SBEST, that integrates stack trace data with test coverage to enhance fault localization. Our approach shows a significant improvement, increasing Mean Average Precision (MAP) by 32.22% and Mean Reciprocal Rank (MRR) by 17.43% over traditional stack trace ranking methods.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Barreto Simedo Pacheco, Lorena
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Software Engineering
Date:25 June 2024
Thesis Supervisor(s):Chen, Tse-Hsun (Peter) and Yang, Jinqiu
ID Code:994058
Deposited By: Lorena Barreto Simedo Pacheco
Deposited On:24 Oct 2024 18:48
Last Modified:24 Oct 2024 18:48
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