Open Access

ARTICLE

Multi-Objective Adapted Binary Bat for Test Suite Reduction

by Nagwa Reda¹, Abeer Hamdy^2,*, Essam A. Rashed^1,3

1 Department of Mathematics, Faculty of Science, Suez Canal University, Egypt
2 Faculty of Informatics and Computer Science, British University in Egypt, Egypt
3 Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Japan

* Corresponding Author: Abeer Hamdy. Email:

(This article belongs to the Special Issue: Soft Computing Methods for Innovative Software Practices)

Intelligent Automation & Soft Computing 2022, 31(2), 781-797. https://doi.org/10.32604/iasc.2022.019669

Received 21 April 2021; Accepted 16 June 2021; Issue published 22 September 2021

Abstract

Regression testing is an essential quality test technique during the maintenance phase of the software. It is executed to ensure the validity of the software after any modification. As software evolves, the test suite expands and may become too large to be executed entirely within a limited testing budget and/or time. So, to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by discarding the redundant test cases and selecting the most representative ones that do not compromise the effectiveness of the test suite in terms of some predefined criteria such as its fault-detection capability. This problem is known as test suite reduction (TSR); and it is known to be as nondeterministic polynomial-time complete (NP-complete) problem. This paper formulated the TSR problem as a multi-objective optimization problem; and adapted the heuristic binary bat algorithm (BBA) to resolve it. The BBA algorithm was adapted in order to enhance its exploration capabilities during the search for Pareto-optimal solutions. The effectiveness of the proposed multi-objective adapted binary bat algorithm (MO-ABBA) was evaluated using 8 test suites of different sizes, in addition to twelve benchmark functions. Experimental results showed that, for the same fault discovery rate, the MO-ABBA is capable of reducing the test suite size more than each of the multi-objective original binary bat (MO-BBA) and the multi-objective binary particle swarm optimization (MO-BPSO) algorithms. Moreover, MO-ABBA converges to the best solutions faster than each of the MO-BBA and the MO-BPSO.

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Keywords

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