{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T08:56:36Z","timestamp":1781168196290,"version":"3.54.1"},"reference-count":53,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University","award":["IMSIU-DDRSP2604"],"award-info":[{"award-number":["IMSIU-DDRSP2604"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Performance testing is a critical aspect of mobile application development, ensuring responsiveness and reliability under diverse conditions. However, traditional test case selection methods, such as random and coverage-based approaches, often fail to optimize execution while maintaining critical test coverage. Machine learning (ML)-based techniques, though promising, introduce computational overhead and lack interpretability, limiting their effectiveness in real-world testing. To address these challenges, we propose OptiTest+, a Fuzzy Analytical Hierarchy Process (FAHP) based test case prioritization framework that enhances test suite optimization for mobile performance testing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>OptiTest+ integrates FAHP with Five-Level Ranking Thresholds, ranking test cases based on key performance indicators such as response time, throughput, latency, and network load, critical factors in ensuring mobile application efficiency. FAHP leverages expert-driven pairwise comparisons to prioritize high-impact test cases while eliminating redundant ones. The framework was validated across ten real-world mobile applications, including Uber, GitHub, and EasyPaisa. Both manual and automated FAHP calculations were performed to ensure the accuracy of the automated tool, with results cross-verified for consistency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results demonstrate that OptiTest+ reduced test suite size by 35% on average, while preserving 90% of critical performance test coverage. The approach substantially reduced test execution time by 40%, optimizing testing efficiency without compromising the detection of performance bottlenecks. A comparative evaluation against random, coverage-based, Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Pareto-based selection techniques confirmed that FAHP offers a superior balance between test reduction, coverage retention, and computational efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By integrating structured FAHP based decision-making with automated test prioritization, OptiTest+ provides a scalable, interpretable, and efficient alternative to traditional and ML-based selection methods. The findings demonstrate that FAHP can effectively optimize test suite selection for mobile applications, ensuring faster and more resource-efficient performance testing. Future research will explore hybrid FAHP-Machine Learning models to enhance adaptability, automate decision-making, and further improve test selection efficiency for large-scale mobile applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj-cs.3697","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T08:00:22Z","timestamp":1781164822000},"page":"e3697","source":"Crossref","is-referenced-by-count":0,"title":["OptiTest+: a fuzzy analytical hierarchy process (AHP)-based approach for optimizing performance test case selection in mobile applications"],"prefix":"10.7717","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4892-2974","authenticated-orcid":true,"given":"Qurrat Ul","family":"Ain","sequence":"first","affiliation":[{"name":"National University of Computer and Emerging Sciences, Islamabad, 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