Maintainability of Service-Oriented Architecture using Hybrid K-means Clustering Approach

doi:10.23940/ijpe.23.01.p4.3342

Abstract

Abstract: The process of making changes to software after it has been delivered to the client is known as maintainability. Maintainability deals with new or changed client requirements. Service-oriented architecture (SOA) is a method for developing applications that helps services work on different environments. SOA works on patterns of distributed systems that help different applications communicate with each other using different protocols. To assess the maintainability of service-oriented architecture, different factors are required. Some of these factors are analyzability, changeability, stability, and testability. Modification is the process of upgrading the software functionality. After modification of service-oriented architecture, the module will go to the testing phase. The evaluation and verification of whether a software product or application performs as intended is known as testing. The testing phase is a combination of various stages, such as individual module testing and testing after collaborations between them. This testing stage is time-consuming in the maintenance process. The term “outlier" refers to a module in software systems that deviates significantly from the rest of the module. It represents the collection of data, variables, and methods. For instance, the program might have been coded mistakenly or an investigation might not have been run accurately. To detect the outlier module, test cases are needed. A methodology is proposed to reduce the predefined test cases. K-means clustering is the best approach to calculate the number of test cases, but the outlier is not automatically determined. In this paper, a hybrid clustering approach is applied to detect the outlier. This clustering method is used in software testing to count the number of comments in various software and in medical science to diagnose the disease of Covid patients. The experimental outcomes show that our strategy achieves better results.

Key words: clustering approach, k-means clustering, outlier detection, data analysis, Euclidean distance

Arvind Kumar Mishra, Renuka Nagpal, Kirti Seth, and Rajni Sehgal. Maintainability of Service-Oriented Architecture using Hybrid K-means Clustering Approach [J]. Int J Performability Eng, 2023, 19(1): 33-42.

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References

1. Haorongbam L., Nagpal R., andSehgal R,Service Oriented Architecture (SOA): A literature review on the maintainability, approaches and design process. In2022 12th International Conference on Cloud Computing, Data Science and Engineering (Confluence), IEEE, pp. 647-652, 2022
2. Mishra, A. K., Nagpal, R., Seth, K., and Sehgal, R. A critical review on service oriented architecture and its maintainability. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), IEEE, pp. 1-8, 2021.
3. Seth Asish and Seth kirti, Understanding SOA,BPB Publications, pp. 12, 2020.
4. Araujo J., Melo C., Oliveira F., Pereira P., andMatos R.A software maintenance methodology: An approach applied to software aging. In2021 IEEE International Systems Conference (SysCon), IEEE, pp. 1-8, 2021.
5. Zare, H.,Emadi, S.Determination of customer satisfaction using improved K-means algorithm. Soft Computing, vol. 24, no. 22, pp. 16947-16965, 2020.
6. Hossain M. Z., Akhtar M. N., Ahmad R. B., andRahman M.A dynamic K-means clustering for data mining. Indonesian Journal of Electrical engineering and computer science, vol. 13, no. 2, pp. 521-526, 2019.
7. Lin H. Y., Chiu Y. H., Liao W. C., andChang R. I.Service-oriented architecture for intelligent management with data analytics and visualization. In2019 IEEE 12th conference on service-oriented computing and applications (SOCA), IEEE, pp. 73-78, 2019.
8. Mohammadi, M.,Mukhtar, M.Service-oriented architecture and process modeling. In2018 International Conference on Information Technologies (InfoTech), IEEE, pp. 1-4, 2018.
9. Irani J., Pise N., andPhatak M.Clustering techniques and the similarity measures used in clustering: A survey. International journal of computer applications, vol. 134, no. 7, pp. 9-14, 2016.
10. Kumar G. R., Mangathayaru N., andNarasimha G.An improved k-means clustering algorithm for intrusion detection using Gaussian function.In Proceedings of the The International Conference on Engineering and MIS 2015, pp. 1-7, 2015.
11. Behera H. S., Ghosh A., andMishra S. K.A new hybridized K-Means clustering based outlier detection technique for effective data mining. International Journal of Advanced Research in Computer Science and Software Engineering, vol.2, no. 4, pp. 287-289 ,2012.
12. Manikandan. G, Rajendiran. P, Kamarasan. M, SowndaryaShekar, Performance analysis of clustering algorithms in Outlier detection based on statistical models and spatial proximity. International Journal of Computer Science and Information Technologies, vol. 2, no. 4, pp. 1747-1749, 2011.
13. Chakraborty S., Nagwani N. K., andDey L,Performance comparison of incremental k-means and incremental dbscan algorithms.arXiv preprint arXiv:1406.4751, 2014.
14. Patel, V. R.,Mehta, R. G.Impact of outlier removal and normalization approach in modified k-means clustering algorithm. International Journal of Computer Science Issues (IJCSI), vol. 8, no. 5, pp. 331, 2011
15. Al-Zoubi, M.B. An effective clustering-based approach for outlier detection. European Journal of Scientific Research, vol. 28, no. 2, pp.310-316, 2009.
16. Bertolino A.Software testing research: Achievements, challenges, dreams. InFuture of Software Engineering (FOSE'07), IEEE, pp. 85-103, 2007.
17. Zhang, J.,Wang, H.Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance. Knowledge and information systems, vol. 10, no. 3, pp. 333-355, 2006.
18. Ranjan Aritha, Automated requirement based test case generation,Communication of ACM, 2006.
19. Xu, R.,Wunsch, D.Survey of clustering algorithms. IEEE Transactions on neural networks, vol. 16, no. 3, pp. 645-678, 2005.
20. Last M., Friedman M., andKandel A.The data mining approach to automated software testing. InProceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 388-396, 2003.
21. Chaumun M. A., Kabaili H., Keller R. K., andLustman F.A change impact model for changeability assessment in object-oriented software systems. Science of Computer Programming, vol.45, no. 2-3, pp. 155-174, 2002.
22. Iso, I. S. O. Iec9126-1: Software engineering-product quality-part 1: Quality model. Geneva, Switzerland: International Organization for Standardization, vol. 21, 2001.
23. Bourque, D. P.,Dupuis, R.Save offline, Standard for Software Maintenance, IEEE Computer Society Press, Los Alamitos, CA, 1998.
24. Yau, S. S.,Collofello, J. S.Some stability measures for software maintenance. IEEE Transactions on Software Engineering, vol. 6, pp. 545-552, 1980.
25. Sowndarya. S, Refining the clustering of the k-mean clustering algorithm using of the Leader follower algorithm. International Journal of Computer Science and Engineering Technology, vol. 1, no. 4 pp. 92-96.
26. Kumar, V., Kumar, S. and Singh, A.K.Outlier detection: a clustering-based approach. International Journal of Science and Modern Engineering, vol. 1, no. 7, pp.16-19, 2013.
27. Na, S., Xumin, L. and Yong, G.Research on k-means clustering algorithm: An improved k-means clustering algorithm. In2010 Third International Symposium on intelligent information technology and security informatics, IEEE, pp. 63-67, 2010.
28. Stapelberg R.F.Availability and maintainability in engineering design, Springer London, pp. 295-527, 2009
29. https://www.kaggle.com/datasets/imdevskp/corona-virus-report, accessed in November 2022
30. https://www.kaggle.com/datasets/sapal6/the-testcase-dataset, accessed in November 2022

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