Selectively Combining Multiple Coverage Goals in Search-Based Unit Test Generation

Zhou, Z., Zhou, Y., Fang, C., Chen, Z. and Tang, Y. (2022) Selectively Combining Multiple Coverage Goals in Search-Based Unit Test Generation. In: 37th IEEE/ACM International Conference on Automated Software Engineering (ASE '22), Rochester, MI, USA, 10 - 14 October 2022, ISBN 9781450394758 (doi: 10.1145/3551349.3556902)

Abstract

Unit testing is a critical part of software development process, ensuring the correctness of basic programming units in a program (e.g., a method). Search-based software testing (SBST) is an automated approach to generating test cases. SBST generates test cases with genetic algorithms by specifying the coverage criterion (e.g., branch coverage). However, a good test suite must have different properties, which cannot be captured by using an individual coverage criterion. Therefore, the state-of-the-art approach combines multiple criteria to generate test cases. As combining multiple coverage criteria brings multiple objectives for optimization, it hurts the test suites’ coverage for certain criteria compared with using the single criterion. To cope with this problem, we propose a novel approach named smart selection. Based on the coverage correlations among criteria and the coverage goals’ subsumption relationships, smart selection selects a subset of coverage goals to reduce the number of optimization objectives and avoid missing any properties of all criteria. We conduct experiments to evaluate smart selection on 400 Java classes with three state-of-the-art genetic algorithms. On average, smart selection outperforms combining all goals on of the classes having significant differences between the two approaches.

• Item Type: Conference Proceedings
• Additional Information: Z. Zhou and Y. Tang are partially sponsored by Shanghai Pujiang Program (No. 21PJ1410700). Y. Zhou is partially sponsored by National Natural Science Foundation of China (No. 62172205). C. Fang and Z. Chen are partially sponsored by Science, Technology and Innovation Commission of Shenzhen Municipality (CJGJZD20200617103001003).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tang, Dr Yutian
• Authors: Zhou, Z., Zhou, Y., Fang, C., Chen, Z., and Tang, Y.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450394758
• Published Online: 05 January 2023