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.
Highlights
Software testing is one of the crucial activities in the software development lifecycle
RQ2: Does the performance of the multiobjective adapted binary bat algorithm (MO-adapted (modified) binary bat algorithm (ABBA)) surpasses the performance of the MO-binary bat algorithm (BBA) and the multi-objective binary particle swarm optimization (MOBPSO) in solving the MO-test suite reduction (TSR) problem?
This paper proposed solving the multi-objective test suite reduction problem using the Binary Bat algorithm, which is reported in the literature as one of the effective swarm intelligence based algorithms
Summary
Software testing is one of the crucial activities in the software development lifecycle. In order to reduce the cost of regression testing, it is necessary to reduce the number of test cases in the test suite without compromising its effectiveness, in terms of some predefined criteria [4,5]. The majority of these approaches are in the form of a white-box [9] They aim at reducing the test suite without compromising test requirements such as statement coverage, fault detection capability rate, etc. Most of these approaches utilized greedy algorithms to solve a single objective TSR (SO-TSR) problem [8]. Mohanty et al [11] used Ant Colony (ACO) in their proposed approach for solving the MOTSR problem
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.