Pairwise Test Case Generation using (1+1) Evolutionary Algorithm for Software Product Line Testing

• Published in: International journal of advanced computer science & applications 2023, Vol.14 (8)
• Main Authors: Khatir, Sharafeldin Kabashi, Sulaiman, Rabatul Aduni Binti, Azrag, Mohammed Adam Kunna, Zain, Jasni Mohamad, Odili, Julius Beneoluchi, Al-Shami, Samer Ali
• Format: Article
• Language: English
• Subjects: Case studies; Combinatorial analysis; Evolutionary algorithms; Genetic algorithms; Permutations; Product lines; Software; Software development
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2023.0140852

Software product line SPLs, or software product lines, are groups of similar software systems that share some commonalities but stand out from one another in terms of the features they offer. Over the past few decades, SPLs have been the focus of a great deal of study and implementation in both the academic and commercial sectors. Using SPLs has been shown to improve product customization and decrease time to market. Additional difficulties arise when testing SPLs because it is impractical to test all possible product permutations. The use of Combinatorial Testing in SPL testing has been the subject of extensive study in recent years. The purpose of this study is to gather and analyze data on combinatorial testing applications in SPL, apply Pairwise Testing using (1+1) evolutionary algorithms to SPL across four case studies, and assess the algorithms' efficacy using predetermined evaluation criteria. According to the findings, the performance of this technique is superior when the case study is larger, that is, when it has a higher number of features, than when the case study is smaller in scale.