Prioritization of Test Cases in Software Testing Using M2 H2 Optimization

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Author(s)

Kodepogu Koteswara Rao 1,* M Babu Rao 2 Chaduvula Kavitha 2 Gaddala Lalitha Kumari 1 Yalamanchili Surekha 1

1. Dept. of CSE, PVP Siddhartha Institute of Institute of Technology, Vijayawada 520007, India

2. Department of CSE, Gudlavalleru Engineering College, Gudlavalleru 521356, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2022.05.06

Received: 27 Nov. 2021 / Revised: 10 Jan. 2022 / Accepted: 17 Feb. 2022 / Published: 8 Oct. 2022

Index Terms

Testing, H2O, PTC, FDAP.

Abstract

By and large, software testing can be well thought-out as a adept technique of achieving improved software quality as well as reliability. On the other hand, the eminence of the test cases had significant effect on the fault enlightening competence of testing activity. Prioritization of Test case (PTC) remnants one challenging issue, as prioritizing test cases remains not up in the direction of abrasion by means of respect to Faults Detected Average Percentage (FDAP) and time execution results. The PTC is predominantly anticipated to scheme assortment of test cases in accomplishing timely optimization by means of preferred properties. Earlier readings have been presented for place in order the accessible test cases in upsurge speed the fault uncovering rate in testing. In this phase, this learning schemes a Modern modified Harris Hawks Optimization centered PTC (M2H2O-PTC) method for testing. The anticipated M2H2O-PTC method aims to exhaust the possibilities the FDAP and curtail the complete execution time. Besides, the M2H2O algorithm is considered for boosting the examination and taking advantage abilities of the conservative H2O algorithm. For validating the enhanced efficiency of the M2H2O-PTC method, an extensive variety of simulations occur on contradictory standard programs and the outcomes are inspected underneath numerous characteristics. The investigational results emphasized enhanced proficiency of the M2H2O-PTC method in excess of the modern methodologies in standings of dissimilar measures.

Cite This Paper

Kodepogu Koteswara Rao, M Babu Rao, Chaduvula Kavitha, Gaddala Lalitha Kumari, Yalamanchili Surekha, "Prioritization of Test Cases in Software Testing Using M2 H2 Optimization", International Journal of Modern Education and Computer Science(IJMECS), Vol.14, No.5, pp. 56-67, 2022. DOI:10.5815/ijmecs.2022.05.06

Reference

[1]Hao, D., Zhang, L., & Mei, H. (2016). Test-case prioritization: achievements and challenges. Frontiers of Computer Science, 10(5), 769-777.
[2]Khatibsyarbini, M., Isa, M. A., Jawawi, D. N., Hamed, H. N. A., & Suffian, M. D. M. (2019). Test case prioritization using firefly algorithm for software testing. IEEE access, 7, 132360-132373.
[3]Balakiruthiga, B., Deepalakshmi, P., Mohanty, S. N., Gupta, D., Kumar, P. P., & Shankar, K. (2020). Segment routing based energy aware routing for software defined data center. Cognitive Systems Research, 64, 146-163..
[4]Hao, D., Zhang, L., Zang, L., Wang, Y., Wu, X., & Xie, T. (2015). To be optimal or not in test-case prioritization. IEEE Transactions on Software Engineering, 42(5), 490-505.
[5]Porkodi, V., Singh, A. R., Sait, A. R. W., Shankar, K., Yang, E., Seo, C., & Joshi, G. P. (2020). Resource provisioning for cyber–physical–social system in cloud-fog-edge computing using optimal flower pollination algorithm. ieee access, 8, 105311-105319.
[6]W. Jun, Z. Yan and J. Chen, “Test case prioritization technique based on genetic algorithm,” in 2011 International Conference on Internet Computing and Information Services, Hong Kong, China, pp. 173-175, 2011.
[7]S. K. Lakshmanaprabu, S. N. Mohanty, S. S. Rani, S. Krishnamoorthy, J. Uthayakumar et al. “Online clinical decision support system using optimal deep neural networks,” Applied Soft Computing, vol. 81, p. 105487, Aug. 2019.
[8]Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017, July). Reinforcement learning for automatic test case prioritization and selection in continuous integration. In Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 12-22).
[9]Panwar, D., Tomar, P., & Singh, V. (2018). Hybridization of Cuckoo-ACO algorithm for test case prioritization. Journal of Statistics and Management Systems, 21(4), 539-546.
[10]Miranda, B., Cruciani, E., Verdecchia, R., & Bertolino, A. (2018, May). FAST approaches to scalable similarity-based test case prioritization. In 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) (pp. 222-232). IEEE.
[11]Ali, S., Hafeez, Y., Hussain, S., & Yang, S. (2020). Enhanced regression testing technique for agile software development and continuous integration strategies. Software Quality Journal, 28(2), 397-423.
[12]Al-Hajjaji, M., Thüm, T., Lochau, M., Meinicke, J., & Saake, G. (2019). Effective product-line testing using similarity-based product prioritization. Software & Systems Modeling, 18(1), 499-521.
[13]Xing, Y., Wang, X., & Shen, Q. (2021). Test case prioritization based on Artificial Fish School Algorithm. Computer Communications, 180, 295-302.
[14]Gokilavani, N., & Bharathi, B. (2021). Multi-Objective based test case selection and prioritization for distributed cloud environment. Microprocessors and Microsystems, 82, 103964.
[15]Sivaji, U., & Rao, P. S. (2021). Test case minimization for regression testing by analyzing software performance using the novel method. Materials Today: Proceedings.
[16]Khalilian, A., Azgomi, M. A., & Fazlalizadeh, Y. (2012). An improved method for test case prioritization by incorporating historical test case data. Science of Computer Programming, 78(1), 93-116.
[17]Houssein, E. H., Hosney, M. E., Elhoseny, M., Oliva, D., Mohamed, W. M., & Hassaballah, M. (2020). Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics. Scientific Reports, 10(1), 1-22.
[18]Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
[19]Zhang, Y., Zhou, X., & Shih, P. C. (2020). Modified Harris Hawks optimization algorithm for global optimization problems. Arabian Journal for Science and Engineering, 45(12), 10949-10974..
[20]J. H. Kwon, I. Y. Ko, G. Rothermel and M. Staats, “Test case prioritization based on information retrieval concepts,” in 2014 21st Asia-Pacific Software Engineering Conference, Jeju, South Korea, pp. 19–26, 2014.
[21]Rao, K. K., Raju, G. S. V. P., & Nagaraj, S. (2013). Optimizing the software testing efficiency by using a genetic algorithm: a design methodology. ACM SIGSOFT Software Engineering Notes, 38(3), 1-5.
[22]Rao, K. K., & Raju, G. S. V. P. (2015). Developing optimal directed random testing technique to reduce interactive faults-systematic literature and design methodology. Indian Journal of Science and Technology, 8(8), 715.
[23]Rao, K.K., Saroja, Y., Babu, N.R., Kumari, G.L., Surekha, Y.”Adaptive genetic algorithm (Aga) based optimal directed random testing for reducing interactive faults 2021, 12(2), pp. 485–498
[24]Koteswara Rao, K., & Raju, G. S. V. P. (2019). Reducing interactive fault proneness in software application using genetic algorithm based optimal directed random testing. International Journal of Computers and Applications, 41(4), 296-305.
[25]Koteswara Rao, K., Anil Kumar, P., Chandra Mohan, C.”Software test case generation and it’s curtail using G-genetic algorithm “International Journal of Recent Technology and Engineering, 2019, 8(2), pp. 852–855