Vol.29, No.1, 2021, pp.307-318, doi:10.32604/iasc.2021.017239
Optimizing the Software Testing Problem Using Search-Based Software Engineering Techniques
  • Hissah A. Ben Zayed, Mashael S. Maashi*
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Kingdom of Saudi Arabia
* Corresponding Author: Mashael S. Maashi. Email:
(This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)
Received 25 January 2021; Accepted 03 April 2021; Issue published 12 May 2021
Software testing is a fundamental step in the software development lifecycle. Its purpose is to evaluate the quality of software applications. Regression testing is an important testing methodology in software testing. The purpose of regression testing is to validate the software after each change of its code. This involves adding new test cases to the test suite and running the test suite as the software changes, making the test suite larger. The cost and time of the project are affected by the test suite size. The challenge is to run regression testing with a smaller number of test cases and larger amount of software coverage. Minimization of the test suite with maximization of the software coverage is an NP-complete problem. Search-based software engineering is an important topic in software engineering, which addresses software engineering optimization problems to find the optimal/approximate solution of the given problem. This study investigated an approach to reducing the regression testing effort and saving time. It also solved the regression testing optimization problem by achieving the maximum test suite coverage with the minimum test suite size. Several experiments were conducted to obtain the optimal solutions for the regression testing problem. We propose an optimization methodology that combines a genetic algorithm and a greedy algorithm to optimize regression testing by respectively maximizing the software test coverage and minimizing the test suite size. The proposed methodology can conveniently deliver fault-free, fully covered, and powerful programs for mission-critical functions. It can be applied to test a real-time system that has high requirements for reliability, security, and safety.
Software testing; test suite coverage; regression testing; search-based software engineering optimization; genetic algorithm; greedy algorithm; optimization
Cite This Article
H. A. and M. S. Maashi, "Optimizing the software testing problem using search-based software engineering techniques," Intelligent Automation & Soft Computing, vol. 29, no.1, pp. 307–318, 2021.
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