Enriching Automatic Test Case Generation by Extracting Relevant Test Inputs from Bug Reports

• URL: http://arxiv.org/abs/2312.14898v1
• Date: Fri, 22 Dec 2023 18:19:33 GMT
• Title: Enriching Automatic Test Case Generation by Extracting Relevant Test Inputs from Bug Reports
• Authors: Wendk\^uuni C. Ou\'edraogo, Laura Plein, Kader Kabor\'e, Andrew Habib, Jacques Klein, David Lo, Tegawend\'e F. Bissyand\'e
• Abstract summary: name is a technique for exploring bug reports to identify input values that can be fed to automatic test generation tools. For Defects4J projects, our study has shown that name successfully extracted 68.68% of relevant inputs when using regular expression in its approach.
• Score: 8.85274953789614
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The quality of a software is highly dependent on the quality of the tests it is submitted to. Writing tests for bug detection is thus essential. However, it is time-consuming when done manually. Automating test cases generation has therefore been an exciting research area in the software engineering community. Most approaches have been focused on generating unit tests. Unfortunately, current efforts often do not lead to the generation of relevant inputs, which limits the efficiency of automatically generated tests. Towards improving the relevance of test inputs, we present \name, a technique for exploring bug reports to identify input values that can be fed to automatic test generation tools. In this work, we investigate the performance of using inputs extracted from bug reports with \name to generate test cases with Evosuite. The evaluation is performed on the Defects4J benchmark. For Defects4J projects, our study has shown that \name successfully extracted 68.68\% of relevant inputs when using regular expression in its approach versus 50.21\% relevant inputs without regular expression. Further, our study has shown the potential to improve the Line and Instruction Coverage across all projects. Overall, we successfully collected relevant inputs that led to the detection of 45 bugs that were previously undetected by the baseline.

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