Performance Evaluation of “Ga-Fc” Technique for Aspect-Oriented Software System

Susheela Hooda; Vikas Lamba; Amandeep Kaur

doi:10.17485/IJST/v14i41.1141

Article

Performance Evaluation of “Ga-Fc” Technique for Aspect-Oriented Software System

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i41.1141

Year: 2021, Volume: 14, Issue: 41, Pages: 3074-3081

Original Article

Performance Evaluation of “Ga-Fc” Technique for Aspect-Oriented Software System

Susheela Hooda^1*, Vikas Lamba¹, Amandeep Kaur¹

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

*Corresponding Author
Email: [email protected]

Received Date:21 June 2021, Accepted Date:08 November 2021, Published Date:04 December 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives : Exhaustive testing requires more effort and consumes lots of time of the software testers. Recently, “GA-FC” technique has been proposed to generate test cases optimally for Aspect-Oriented Software System (AOSS). This paper evaluates the performance of the proposed “GA-FC” technique. Method: To analyze the performance of “GA-FC” technique, two parameters namely, Effectiveness of Test Suite Minimization (ETSM) and Aspectual Branch Coverage (ABC) have been used against GA and FC technique individually. Findings: - GA-FC technique has been applied on three case studies and the obtained results reveal that GA-FC technique reduces the testing efforts by producing the minimum number of test cases and time of the tester. Novelty: “GA-FC” technique generates the minimum number of test cases by composition of metaheuristic techniques.

Keywords: AspectOriented Software System; Aspectual Branch Coverage; Genetic Algorithm; Fuzzy Clustering Algorithm; AspectOriented Software Testing

References

Chauhan N. Software Testing Principles and Practices (2). Oxford University Press. 2016.
Pressman RS, Maxim BR. Software Engineering: Practitioner’s Approach (8). 2019.
Jallote P. An Integrated Approach to Software Engineering (2). Narosa Publishing House. 1998.
Chhabra R, Verma S, Krishna CR. A survey on driver behavior detection techniques for intelligent transportation systems. 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence. 2017;p. 36–47. Available from: https://doi.org/10.1109/CONFLUENCE.2017.7943120
Boluwaji A, Gboyega D, Nwokoro C. A Systematic Review of Soft Computing Techniques for Software Testing. International Journal of Computer Science & Management Studies. 2019;40(1):7–17. Available from: https://www.researchgate.net/publication/336837415
Jyoti , SH. A Systematic Review and Comparative study of existing testing techniques for Aspect-oriented software systems. International Research Journal of Engineering and Technology. 2017;4(5):879–888. Available from: https://www.irjet.net/archives/V4/i5/IRJET-V4I5175.pdf
Harrold MJ, Gupta R, Soffa ML. A methodology for controlling the size of a test suite. Proceedings. Conference on Software Maintenance 1990. 1990.
Jain M, Gopalani D. Aspect-Oriented Approach for Testing Software Applications and Automatic Aspect Creation. International Journal of Software Engineering and Knowledge Engineering. 2019;29(10):1379–1402. doi: 10.1142/s0218194019500438
Kaur C, Garg S. Testing Aspect-Oriented Software Using UML Activity Diagram. International Journal of Engineering and Technology. 2012;1(3).
Madadpour S, Hosseinabadi S. Testing Aspect-Oriented Software with UML Activity Diagram. International Journal of Computer Applications. 2011;(8) 33.
Yoo S, Harman M. Regression testing minimization, selection and prioritization: a survey. Software Testing, Verification and Reliability. 2012;22(2):67–120. doi: 10.1002/stv.430
Rhmann W, Zaidi T, Saxena V. Use of Genetic Approach for Test Case Prioritization from UML Activity Diagram. International Journal of Computer Applications. 2015;115(4):8–12. doi: 10.5120/20137-2232
Srividhya J, Gunsundari R. Test Suite Minimization and Empirical Analysis of Optimization Algorithms. Journal of Theoretical and Applied Information Technology. 2016;(1) 94.
Suresh Y, Rath SK. Genetic Algorithms Based Approach for Test Data Generation in Basis Path Testing. Journal of Software Computing and Software Engineering. 2013;3(3).
Xia C, Zhang Y, Hui Z. Test Suite Reduction via Evolutionary Clustering. IEEE Access. 2021;9(9):28111–28121. doi: 10.1109/access.2021.3058301
Harman M, Islam F, Xie T, Wappler S. Automated Test Data Generation for Aspect-Oriented Program. International Conference AOSD. 2009. Available from: https://doi.org/10.1145/1509239.1509264
Biswal BN, Barpanda SS, Mohapatra DP. A Novel Approach for Optimized Test Case Generation Using Activity and Collaboration Diagram. International Journal of Computer Applications. 2010;1(14):67–71. doi: 10.5120/299-463
DS, Hooda S. A Novel Approach for Testing an Aspect-Oriented Software System Using Prioritized-Genetic Algorithm (P-GA) International Journal of Applied Engineering Research. 2017;12(21). Available from: https://www.ripublication.com/ijaer17/ijaerv12n21_103.pdf
DS, Hooda S. Test Suite Minimization using Fuzzy Clustering for Aspect-Oriented Software System. IOSR-Journal of Engineering. 2018;8(9):44–51. Available from: http://www.iosrjen.org/Papers/vol8_issue9/Version-2/H0809024450.pdf
Delamare R, Kraft NA. A Genetic Algorithm for Computing Class Integration Test Orders for Aspect-Oriented Systems. 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation. 2012;p. 804–813. Available from: https://doi.org/10.1109/ICST.2012.179
Kumar G, Bhatia PK. Software testing optimization through test suite reduction using fuzzy clustering. CSI Transactions on ICT. 2013;1(3):253–260. Available from: https://dx.doi.org/10.1007/s40012-013-0023-3
Kaur A, Grover PS, Dixit A. A framework for evaluating extensibility in Aspect-Oriented Software System and its Validation. Recent Patents on Engineering. 2020;14(4). Available from: https://doi.org/10.2174/1872212113666190625115111.2020
Xie T, Zhao J. A Framework and Tool Support for Generation of Test Inputs to AspectJ Programs. . Proceeding of the 5th International Conference on Aspect-Oriented Software Development. 2006;p. 190–201. Available from: https://doi.org/10.1145/1119655.1119681
Babu C, Krishnan HR. Fault model and test-case generation for the composition of aspects. ACM SIGSOFT Software Engineering Notes. 2009;34(1):1–6. Available from: https://dx.doi.org/10.1145/1457516.1457521
Cui Z, Wang L, Li X, Xu D. Modeling and integrating aspects with UML activity diagrams. Proceedings of the 2009 ACM symposium on Applied Computing - SAC '09. 2009;p. 430–437. Available from: https://doi.org/10.1145/1529282.1529377
Dalal S, Hooda S. A Novel Technique for Testing an Aspect Oriented Software System using Genetic and Fuzzy Clustering Algorithm. In: 2017 International Conference on Computer and Applications (ICCA). IEEE. 2017.

Copyright

© 2021 Hooda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)