Maintaining a software system includes tasks such as fixing defects, adding new features, or modifying the software (software changes) to accommodate different environments. Then, the modified software system needs to be tested, to ensure the changes will not having any adverse effects on the previously validated code. Regression testing is one of the approaches which software tester used to test the software system. The traditional regression testing strategy was to repeat all the previous tests and retesting all the features of the program even for small modifications. For programming with thousand lines of codes (LOC), the cost of retesting the entire system is expensive if attempted after every change. This practice is becoming increasingly difficult because of the demand for testing the new functionalities and correcting errors with limited resources. Numerous techniques and tools have been proposed and developed to reduce the costs of regression testing and to aid regression testing processes, such as test suite reduction, test case prioritization, and test case done on the thresholds and weightings used in regression testing. However, there is still need to study on the software traceability model of coverage analysis in software changes during regression testing and test effort estimation on regression testing. Hence, this paper describes the proposal for improving software changes with hybrid traceability model and test effort estimation during regression testing. We will explain our proposed work including the problem background, the intended research objectives, literature review and plan for future implementation. This study is expected to contribute in developing hybrid traceability model for large software development project to support software changes during regression testing with test estimation approach and expected to reduce operational cost during the implementation on software maintenance. Also, it is hoped that an efficient and improve solution to regression testing can be realized, thus, gives the benefits to software testers and project manager manage the software maintenance task since it is a critical part in software project development
Aggarwal, K. K., Singh, Y., Kaur, A., & Malhotra, R. (2007). Investigating effect of Design Metrics on Fault Proneness in Object-Oriented Systems. Journal of Object Technology, 6(10), 127-141.
Aranha, E., & Borba, P. (2007, September). Test effort estimation models based on test specifications. In Testing: Academic and Industrial Conference Practice and Research Techniques-MUTATION, 2007. TAICPART-MUTATION 2007 (pp. 67-71). IEEE.
Bennett, K. H., & Rajlich, V. T. (2000, May). Software maintenance and evolution: a roadmap. In Proceedings of the Conference on the Future of Software Engineering (pp. 73-87). ACM.
Cleland-Huang, J., Gotel, O. C., Hayes, H. J., Mäder, P., & Zisman, A. (2014, May). Software traceability: trends and future directions. In Proceedings of the on Future of Software Engineering (pp. 55-69). ACM.
Kama, N., Basri, S., Asl, M. H., & Ibrahim, R. (2014). COCHCOMO: A Change Effort Estimation Tool for Software Development Phase. In SoMeT (pp. 1029-1045)
Kushwaha, D. S., & Misra, A. K. (2008). Software test effort estimation. ACM SIGSOFT Software Engineering Notes, 33(3), 6.
Lam, W., & Shankararaman, V. (1999). Requirements change: a dissection of management issues. In EUROMICRO Conference, 1999. Proceedings. 25th (Vol. 2, pp. 244-251). IEEE.
Lehman, M. M. (1980). Programs, life cycles, and laws of software evolution. Proceedings of the IEEE, 68(9), 1060-1076.
Li, D., Li, L., Kim, D., Bissyandé, T. F., Lo, D., & Traon, Y. L. (2016). Watch out for this commit! A study of influential software changes. arXiv preprint arXiv:1606.03266.
Nageswaran, S. (2001). Test effort estimation using use case points. In Quality Week (Vol. 6, pp. 1-6).
Naslavsky, L., & Richardson, D. J. (2007). Using traceability to support model-based regression testing. In Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering (pp. 567-570). ACM.
Nurmuliani, N., Zowghi, D., & Powell, S. (2004). Analysis of requirements volatility during software development life cycle. In Software Engineering Conference, 2004. Proceedings. 2004 Australian (pp. 28-37). IEEE.
Ramler, R., Salomon, C., Buchgeher, G., & Lusser, M. (2017). Tool Support for Change-Based Regression Testing: An Industry Experience Report. In International Conference on Software Quality (pp. 133-152). Springer, Cham.
Rochimah, S., Kadir, W. M. W., & Abdullah, A. H. (2007). An evaluation of traceability approaches to support software evolution. In Software Engineering Advances, 2007. ICSEA 2007. International Conference on (pp. 19-19). IEEE.
Rosero, R. H., Gómez, O. S., & Rodríguez, G. (2016). 15 years of software regression testing techniques—A survey. International Journal of Software Engineering and Knowledge Engineering, 26(05), 675-689.
Shahid, M., & Ibrahim, S. (2016). Change impact analysis with a software traceability approach to support software maintenance. In Applied Sciences and Technology (IBCAST), 2016 13th International Bhurban Conference on (pp. 391-396). IEEE.
Sharma, A., & Kushwaha, D. S. (2012). Applying requirement based complexity for the estimation of software development and testing effort. ACM SIGSOFT Software Engineering Notes, 37(1), 1-11.
Stuebing, H. G. (1984). A software engineering environment (SEE) for weapon system software. IEEE transactions on software engineering, (4), 384-397.
The Standish Group, Chaos, Standish Group Report, 2004.
Zhu, X., Zhou, B., Wang, F., Qu, Y., & Chen, L. (2008, September). Estimate test execution effort at an early stage: An empirical study. In Cyberworlds, 2008 International Conference on (pp. 195-200). IEEE.
In-Text Citation: (Rejab, Chuprat, & Azmi, 2018)
To Cite this Article: Rejab, M. M., Chuprat, S., & Azmi, N. F. M. (2018). A Review for Improving Software Change using Traceability Model with Test Effort Estimation. International Journal of Academic Research in Business and Social Sciencest, 8(4), 1178–1188.
Copyright: © 2018 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
