{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:04:32Z","timestamp":1779887072133,"version":"3.53.1"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","doi-asserted-by":"publisher","award":["PNURSP2022R178"],"award-info":[{"award-number":["PNURSP2022R178"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm\u2019s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.<\/jats:p>","DOI":"10.3390\/s22124374","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T02:01:44Z","timestamp":1655085704000},"page":"4374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8563-6611","authenticated-orcid":false,"given":"Anu","family":"Bajaj","sequence":"first","affiliation":[{"name":"Machine Intelligence Research Labs (MIR Labs), Auburn, WA 98071, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0169-6738","authenticated-orcid":false,"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"Machine Intelligence Research Labs (MIR Labs), Auburn, WA 98071, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saroj","family":"Ratnoo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar 125001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6910-6861","authenticated-orcid":false,"given":"Lubna Abdelkareim","family":"Gabralla","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, College of Applied, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Webert, H., D\u00f6\u00df, T., Kaupp, L., and Simons, S. (2022). Fault Handling in Industry 4.0: Definition, Process and Applications. Sensors, 22.","DOI":"10.3390\/s22062205"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1002\/stvr.430","article-title":"Regression testing minimization, selection and prioritization: A survey","volume":"22","author":"Yoo","year":"2012","journal-title":"Softw. Testing Verif. Reliab."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107584","DOI":"10.1016\/j.asoc.2021.107584","article-title":"Discrete cuckoo search algorithms for test case prioritization","volume":"110","author":"Bajaj","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TSE.2007.38","article-title":"Search algorithms for regression test case prioritization","volume":"33","author":"Li","year":"2007","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11334-021-00384-9","article-title":"Tri-level regression testing using nature-inspired algorithms","volume":"17","author":"Bajaj","year":"2021","journal-title":"Innov. Syst. Softw. Eng."},{"key":"ref_6","first-page":"62","article-title":"Prioritizing and Minimizing Test Cases Using Dragonfly Algorithms","volume":"13","author":"Bajaj","year":"2021","journal-title":"Int. J. Comput. Inf. Syst. Ind. Manag. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1802492","DOI":"10.1155\/2021\/1802492","article-title":"Multiobjective Core Reloading Pattern Optimization of PARR-1 Using Modified Genetic Algorithm Coupled with Monte Carlo Methods","volume":"2021","author":"Shaukat","year":"2021","journal-title":"Sci. Technol. Nucl. Install."},{"key":"ref_8","unstructured":"Fister, J.I., Yang, X.S., Fister, I., Brest, J., and Fister, D. (2013). A brief review of nature-inspired algorithms for optimization. arXiv, 116\u2013122."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, Y., Zhang, X., Wei, B., Zhang, M., and Dou, Z. (2019, January 10\u201312). On test case prioritization using ant colony optimization algorithm. Proceedings of the 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), Zhangjiajie, China.","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00388"},{"key":"ref_10","first-page":"737","article-title":"Test case minimization approach using fault detection and combinatorial optimization techniques for configuration-aware structural testing","volume":"19","author":"Ahmed","year":"2016","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_11","first-page":"2088","article-title":"Test case reduction using ant colony optimization for object oriented program","volume":"5","author":"Mohapatra","year":"2015","journal-title":"Int. J. Electrical. Comput. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y.N., Yang, H., Lin, Z.K., Dai, Q., and Li, Y.F. (2017, January 21\u201323). A test suite reduction method based on novel quantum ant colony algorithm. Proceedings of the 2017 4th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China.","DOI":"10.1109\/ICISCE.2017.176"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mondal, D., Hemmati, H., and Durocher, S. (2015, January 13\u201317). Exploring test suite diversification and code coverage in multi-objective test case selection. Proceedings of the 2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST), Graz, Austria.","DOI":"10.1109\/ICST.2015.7102588"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7221","DOI":"10.1166\/asl.2018.12918","article-title":"Particle swarm optimization for test case prioritization using string distance","volume":"24","author":"Khatibsyarbini","year":"2018","journal-title":"Adv. Sci. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4887","DOI":"10.1016\/j.eswa.2013.02.018","article-title":"Search based constrained test case selection using execution effort","volume":"40","author":"Barros","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"De Souza, L.S., Prud\u00eancio, R.B., and Barros, F.D.A. (2014, January 18\u201322). A hybrid binary multi-objective particle swarm optimization with local search for test case selection. Proceedings of the Brazilian conference on intelligent systems, Sao Paulo, Brazil.","DOI":"10.1109\/BRACIS.2014.80"},{"key":"ref_17","first-page":"1","article-title":"A hybrid particle swarm optimization and harmony search algorithm approach for multi-objective test case selection","volume":"21","year":"2015","journal-title":"J. Braz. Comput. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Correia, D. (2019, January 26\u201330). An industrial application of test selection using test suite diagnosability. Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Tallinn, Estonia.","DOI":"10.1145\/3338906.3342493"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJAMC.2022010106","article-title":"Hybrid particle swarm and ranked firefly metaheuristic optimization-based software test case minimization","volume":"13","author":"Bharathi","year":"2022","journal-title":"Int. J. Appl. Metaheuristic Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1108\/IJICC-04-2019-0038","article-title":"Modified condition decision coverage criteria for test suite prioritization using particle swarm optimization","volume":"12","author":"Nayak","year":"2019","journal-title":"Int. J. Intell. Comput. Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Deneke, A., Assefa, B.G., and Mohapatra, S.K. (2022). Test suite minimization using particle swarm optimization. Mater. Today Proc., 1\u20135.","DOI":"10.1016\/j.matpr.2021.12.472"},{"key":"ref_22","first-page":"9988987","article-title":"Multiobjective Test Case Prioritization Using Test Case Effectiveness: Multicriteria Scoring Method","volume":"2021","author":"Samad","year":"2021","journal-title":"Sci. Program."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Agrawal, A.P., and Kaur, A. (2018). A comprehensive comparison of ant colony and hybrid particle swarm optimization algorithms through test case selection. Data Engineering and Intelligent Computing, Springer.","DOI":"10.1007\/978-981-10-3223-3_38"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"121894","DOI":"10.1109\/ACCESS.2021.3109286","article-title":"Reducing CO2 Emissions of an Airport Baggage Handling Transport System Using a Particle Swarm Optimization Algorithm","volume":"9","author":"Lodewijks","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","unstructured":"Sun, J., Xu, W., and Feng, B. (2004, January 1\u20133). A global search strategy of quantum-behaved particle swarm optimization. Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, Singapore."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1080\/00401706.2018.1439405","article-title":"d-qpso: A quantum-behaved particle swarm technique for finding d-optimal designs with discrete and continuous factors and a binary response","volume":"61","author":"Lukemire","year":"2019","journal-title":"Technometrics"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Iliyasu, A.M., and Fatichah, C. (2017). A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection. Sensors, 17.","DOI":"10.20944\/preprints201711.0193.v1"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Peng, C., Yan, J., Duan, S., Wang, L., Jia, P., and Zhang, S. (2016). Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model. Sensors, 16.","DOI":"10.3390\/s16040520"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15198","DOI":"10.3390\/s150715198","article-title":"A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance","volume":"15","author":"Guo","year":"2015","journal-title":"Sensors"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wen, T., Yan, J., Huang, D., Lu, K., Deng, C., Zeng, T., Yu, S., and He, Z. (2018). Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing. Sensors, 18.","DOI":"10.3390\/s18020388"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1016\/j.eswa.2009.06.044","article-title":"Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems","volume":"37","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11312","DOI":"10.1016\/j.eswa.2009.03.006","article-title":"Quantum behaved particle swarm optimization (QPSO) for multi-objective design optimization of composite structures","volume":"36","author":"Omkar","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_33","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1049\/sfw2.12054","article-title":"A synergic quantum particle swarm optimisation for constrained combinatorial test generation","volume":"16","author":"Guo","year":"2022","journal-title":"IET Softw."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nabi, S., Ahmad, M., Ibrahim, M., and Hamam, H. (2022). AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing. Sensors, 22.","DOI":"10.3390\/s22030920"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Madureira, A., Abraham, A., Gamboa, D., and Novais, P. (2017). Test Suite Prioritization Using Nature Inspired Meta-Heuristic Algorithms. Intelligent Systems Design and Applications, Springer. Advances in Intelligent Systems and Computing.","DOI":"10.1007\/978-3-319-53480-0"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TSE.2010.58","article-title":"The effects of time constraints on test case prioritization: A series of controlled experiments","volume":"36","author":"Do","year":"2010","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bajaj, A., and Sangwan, O.P. (2019, January 26\u201328). Study the impact of parameter settings and operators role for genetic algorithm based test case prioritization. Proceedings of the International Conference on Sustainable Computing in Science, Technology and Management, Jaipur, India.","DOI":"10.2139\/ssrn.3356318"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/32.988497","article-title":"Test case prioritization: A family of empirical studies","volume":"28","author":"Elbaum","year":"2002","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_40","first-page":"1","article-title":"Improved novel bat algorithm for test case prioritization and minimization","volume":"6","author":"Bajaj","year":"2022","journal-title":"Soft Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:26:54Z","timestamp":1760138814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,9]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22124374"],"URL":"https:\/\/doi.org\/10.3390\/s22124374","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,9]]}}}