An Efficient Requirementbased Test Case Prioritization Technique using Optimized TFCSVM Approach

An Efficient Requirement-based Test Case Prioritization Technique using Optimized TFC-SVM Approach

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-1
Year of Publication : 2021
Authors : Omdev Dahiya, Kamna Solanki
DOI :  10.14445/22315381/IJETT-V69I1P202

Citation 

MLA Style: Omdev Dahiya, Kamna Solanki  "An Efficient Requirement-based Test Case Prioritization Technique using Optimized TFC-SVM Approach" International Journal of Engineering Trends and Technology 69.1(2021):5-16. 

APA Style:Omdev Dahiya, Kamna Solanki. An Efficient Requirement-based Test Case Prioritization Technique using Optimized TFC-SVM Approach  International Journal of Engineering Trends and Technology, 69(1),5-16.

Abstract
Software testing is an essential and challenging part of the SDLC (Software Development Life Cycle). Requirement-based TCP (Test case prioritization) is a method to optimize the execution time, cost, and effort as an essential part of the regression testing. It is a technique used to arrange the test cases (TCs), and sorting of the TC’s is based on some criteria. It is established to execute the high priority test cases initially to reduce the execution time, efforts, and cost during the software testing. Thus, conventional TCP (Test Case Prioritization) is motivated to design for testing the software to enhance prioritization efficiency. TCP permits the testers to classify the test cases as the priority for performing the test execution. It helps in enhancing software quality. In the existing research, the authors had developed a method to prioritize the optimal test cases using the firefly approach. They used the firefly algorithm to optimize the ordering of the test cases and fitness value (FV), defined through the same distance model, to have better performance. The firefly’s approach may be more efficient in determining fault proneness problems, which is intensely required in security-critical schemes. Thus, the proposed research deals with the processing of the non-linear approach that provides high classification rates. A TFC-SVM algorithm is a novel approach deal with the CUCKOO optimization in collaboration with SVM, used to achieve higher classification rates in terms of high mean, median, and low minimum value. Afterward, training and testing modules are considered through the classification approach and processed the requirements-based in TCP. The proposed model has resolved the existing issues such as error rates, high priorities, and maximum execution time to prioritize processed requirement-based on test case prioritization. The proposed parameters are evaluated using computation time, APFD, Mean, Standard Deviation, Min, and Max values through which the performance metrics can be achieved for the robust proposed system.

Reference
[1] M. Khatibsyarbini, M. A. Isa, and D. N. A. Jawawi, Test case prioritization approach in regression testing: A systematic literature review.
[2] Information and Software Technology, 93(2)(2018) 74-93.
[3] G. J. Myers, T. Badgett, T. M. Thomas, C. Sandler, The art of software testing.. Chichester: John Wiley & Sons, 2,(2004).
[4] G. Rothermel, R. H. Untch, C. Chu and M. J. Harrold, Test case prioritization: An empirical study. In Proceedings IEEE International Conference on Software Maintenance-1999 (ICSM`99).` Software Maintenance for Business Change`(Cat. No. 99CB36360), IEEE. (1999) 179-188.
[5] D. Hao, L. Zhang, and H. Mei, Test-case prioritization: achievements and challenges. Frontiers of Computer Science 10(5) (2016) 769-777.
[6] O. Dahiya & K. Solanki, A systematic literature study of regression test case prioritization approaches, International Journal of Engineering & Technology, 7(4)(2018) 2184-2191.
[7] O. Dahiya, K. Solanki, S. Dalal, and A. Dhankhar, Regression Testing: Analysis of its Techniques for Test Effectiveness, International Journal of advanced trends in computer science and engineering, 9(1)(2020) 737-744.
[8] A. Ansari, A. Khan, A. Khan, and K. Mukadam, Optimized regression test using test case prioritization. Procedia Computer Science, 79(3)(2016) 152-160.
[9] J. A. P. Lima, & S. R. Vergilio, Test Case Prioritization in Continuous Integration environments: A systematic mapping study. Information and Software Technology,121(1)(2020) 106268.
[10] G. Duggal, & B. Suri. Understanding regression testing techniques. In Proceedings of 2nd National Conference on Challenges and Opportunities in Information Technology. (2008).
[11] X. Wang, H. Zeng, H. Gao, H. Miao, and W. Lin. Location-based test case prioritization for software embedded in mobile devices using the law of gravitation, Mobile Information Systems, (2019) 1-15.
[12] R. Dhiman, & V. Chopra,Novel Approach for Test Case Prioritization Using ACO Algorithm, In 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT), IEEE, 2(3)(2019) 292-295.
[13] M. L. Mohd-Shafie, W. M. N. Wan-Kadir, M. Khatibsyarbini and M. A. Isa, “Model-based test case prioritization using selective and even-spread count-based methods with scrutinized ordering criterion”, Plos one, 15(2)(2020) 1-27.
[14] L. Xiao, H. Miao, T. Shi, and Y. Hong, LSTM based deep learning for spatial-temporal software testing, Distributed and Parallel Databases, 38(7)(2020) 687–712.
[15] T. Afzal, A. Nadeem, and M. Sindhu, Test Case Prioritization Based on Path Complexity, In 2019 International Conference on Frontiers of Information Technology (FIT), IEEE, (2019)363-3635.
[16] H. Srikanth, & L. Williams, On the economics of requirements-based test case prioritization.ACM SIGSOFT Software Engineering Notes, 30(4)(2005) 1-3.
[17] H. Srikanth, C. Hettiarachchi, and H. Do, Requirements based test prioritization using risk factors: An industrial study. Information and Software Technology, 69(2016) 371-83.
[18] M. Khatibsyarbini, M. A. Isa, D. N. A. Jawawi, HNA Hamed, and M.D.M. Suffian, Test Case Prioritization Using Firefly Algorithm for Software Testing, IEEE Access, 7(3)(2019) 132360-132373.
[19] P. R. Srivatsava, B. Mallikarjun, and X. S. Yang, Optimal test sequence generation using firefly algorithm, Swarm and Evolutionary Computation, 8(3)(2013) 44-53.
[20] N. Iqbal, K. Zafar, and W. Zyad, Multi-objective optimization of test sequence generation using multi-objective firefly algorithm (MOFA). In 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE), IEEE, 3(4)(2014) 214-220.
[21] J. Nayak, B. Naik, and H. S. Behera, A novel nature inspired firefly algorithm with higher order neural network: performance analysis, Engineering Science and Technology, an International Journal. 19(1)(2016) 197-211.
[22] D. P. Mohapatra,Firefly optimization technique-based test scenario generation and prioritization, Journal of applied research and technology, 16(6)(2018) 466-483.
[23] X. S. Yang, & X. He. Firefly algorithm: recent advances and applications, International Journal of swarm intelligence, 1(1)(2013) 36-50.
[24] S. Mondal, Data files for Mahtab: Phase-wise Acceleration of Regression Testing for C, Mendeley Data, V2, DOI: 10.17632/7fvwj88jvm.2, (2019).
[25] O. Dahiya, K. Solanki, and A. Dhankhar, Risk-Based Testing: Identifying, Assessing, Mitigating & Managing Risks Efficiently In Software Testing, International Journal of advanced research in engineering and technology, 11(3)(2020) 192-203.
[26] K. Solanki, Y. Singh, & S. Dalal, A Comparative Evaluation of “m-ACO” Technique for Test Suite Prioritization, Indian Journal of science and technology, 9(30) 1-10.
[27] K. Solanki, Y. Singh, and S. Dalal, Experimental analysis of m-ACO technique for regression testing, Indian Journal of Science and Technology, 9(30) 1-7.
[28] O. Dahiya, & K. Solanki, Prevailing Standards in Requirement-Based Test Case Prioritization: An Overview, ICT Analysis and Applications, (2020) 467-474.
[29] O. Dahiya & K. Solanki, A Study on Identification of Issues and Challenges Encountered in Software Testing, International Conference on Communication & Artificial Intelligence (ICCAI-2020) 17th-18th September (2020).
[30] Palak, & P. Gulia, Ant Colony Optimization Based Test Case Selection for Component Based Software. Int. J. Eng. Technol, 7(4) (2018) 2743-2745.
[31] S. Dalal, & V. Dahiya, A novel technique-absolute high utility itemset mining (Ahuim) algorithm for big data, International Journal of advanced trends in computer science and engineering, 9(5) (2020) 7451–7460.
[32] A. Dhankhar, K. Solanki, A. Rathee and Ashish, Predicting Student’s Performance by using Classification Methods, International Journal of advanced trends in computer science and engineering, 8(4) (2019).
[33] Hemlata & P. Gulia,Novel Algorithm for PPDM of Vertically Partitioned Data. International Journal of Applied Engineering Research, 12(12)(2017) 3090-3096.
[34] A. Chahal, & P. Gulia, Deep Learning: A Predictive IoT Data Analytics Method, International Journal of Engineering Trends and Technology, 68(7)(2020) 25-33.

Keywords
Requirement-based Test Case Prioritization, TFC-SVM method, FA (Firefly Algorithm), and APFD metric.