Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Adaptive Test Suits Generation for Large Configurable Systems using SPEA2 Algorithm

Muhammad Abid Jamil
* ORCID logo ,
Mohamed K. Nour
,
Saud S. Alotaibi
ORCID logo ,
Mohammad Jabed Hussain
,
Syed Mutiullah Hussaini
,
Atif Naseer
Version 1 : Received: 7 August 2023 / Approved: 8 August 2023 / Online: 9 August 2023 (10:46:11 CEST)

A peer-reviewed article of this Preprint also exists.

Jamil, M.A.; Nour, M.K.; Alotaibi, S.S.; Hussain, M.J.; Hussaini, S.M.; Naseer, A. Adaptive Test Suits Generation for Self-Adaptive Systems Using SPEA2 Algorithm. Appl. Sci. 2023, 13, 11324. Jamil, M.A.; Nour, M.K.; Alotaibi, S.S.; Hussain, M.J.; Hussaini, S.M.; Naseer, A. Adaptive Test Suits Generation for Self-Adaptive Systems Using SPEA2 Algorithm. Appl. Sci. 2023, 13, 11324.

Abstract

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Run-time validation techniques are required because complex Self-adaptive systems must consistently offer acceptable behavior for important services. The run-time testing can offer further confidence that a Self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for Self-adaptive systems. To ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the SPEA2 algorithm facilitates both the execution and adaptation of run-time testing operations.

Keywords

search-based software engineering; adaptive systems; configurable systems; multiobjective evolutionary algorithms

Subject

Computer Science and Mathematics, Software

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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