Norasyikin Safieny creator author. text abstract or summary my 2019 2019 monographic eng xii, 90 pages : illustrations ; 30 cm. + 1 CD-ROM Software testing is a critical part of software development. Often, test suite sizes grow significantly with subsequent modifications to the software over time resulting into potential redundancies. Test redundancies are undesirable as they incur costs and are not helpful to detect new bugs. Owing to time, resource constraints, test suite minimization strategies are often sought to remove those redundant test cases in an effort to ensure that each test can cover as much requirements as possible. There are already many works in the literature exploiting the greedy computational algorithms as well as the meta-heuristic algorithms, but no single strategies can claim dominance in term of test reduction over their counterparts. Furthermore, despite much useful work, existing strategies has not sufficiently addressed the hybrid based meta-heuristic strategy for test redundancies application. In order to improve the performance of existing strategies, hybridization is seen as the key to exploit the strength of more than one meta-heuristic algorithm. Given such prospects, this research explores a hybrid test redundancy reduction strategy based on Global Neighbourhood Algorithm and Simulated Annealing (GNA_SA), called tReductGNA_SA. For comparative purposes, this research also implements a non-hybrid GNA test redundancy reduction strategy, called tReductGNA, to ascertain that our hybrid strategy outperforms the non-hybrid ones. Additionally, this work also considers realistic combinations of high numbers of requirements and high number of test cases as case studies. Overall, tReductGNA_SA offers more reduction in most cases and give more diversified solutions as compared tReductGNA and many existing works. Specifically, tReductGNA_SA outperforms others with 66.67% (i.e. 2 out of 3 entries) as compared to others strategy at 33.33% (i.e. 1 out of 3 entries). specialized Norasyikin Safieny Faculty of Computer Systems & Software Engineering Thesis (Master of Science) -- Universiti Malaysia Pahang – 2019 Includes bibliographical references Faculty of Computer System and Software Engineering Dissertations Universities and colleges Disertations Theses THE0008184(Local) UMP 191105 20251125105418.0 eng