Measuring of Software Maintainability Using Adaptive Fuzzy Neural Network
Автор: Mohammad Zavvar, Farhad Ramezani
Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs
Статья в выпуске: 10 vol.7, 2015 года.
Бесплатный доступ
Software maintenance mainly refers to the process of correcting software after delivery. Maintenance process is usually a high percentage of Organizational effort to the whole process of software programs. As a result, the effectiveness of the entire production process and customer satisfaction is dependent on the effectiveness of maintenance activities. Because many factors including type of service, type of product and human factors is dependent on the maintenance process, And the imprecise nature of qualitative factors and sub-criteria leading software maintenance, accurate assessment can be maintained in order to measure the effectiveness of programs seem highly desirable. In this paper, using adaptive fuzzy neural network to provide a method for evaluating the capability of software maintenance conducted after the tests, the root mean square error of the proposed method was equal to 0.34331. The results show that the method is based on adaptive fuzzy neural, maintainability software performance evaluation is appropriate.
Adaptive Fuzzy Neural Network, maintenance, Software, Measuring
Короткий адрес: https://sciup.org/15014801
IDR: 15014801
Список литературы Measuring of Software Maintainability Using Adaptive Fuzzy Neural Network
- EEE, I., Standard G lossary of SoftwareEngineering Terminology. IEEE SoftwareEngineering S tandards & oll ection. I EEE, 1990: p. 610.12-190.
- Mordal, K., et al., Software quality metrics aggregation in industry. Journal of Software: Evolution and Process, 2013. 25(10): p. 1117-1135.
- Ampatzoglou, A., G. Frantzeskou, and I. Stamelos, A methodology to assess the impact of design patterns on software quality. Information and Software Technology, 2012. 54(4): p. 331-346.
- Robertson, S. and J. Robertson, Mastering the requirements process: Getting requirements right. 2012: Addison-wesley.
- McCall, J.A., P.K. Richards, and G.F. Walters, Factors in software quality. volume i. concepts and definitions of software quality. 1977, DTIC Document.
- Boehm, B.W., J.R. Brown, and H. Kaspar, Characteristics of software quality. 1978.
- Grady, R.B. and D.L. Caswell, Software metrics: establishing a company-wide program. 1987.
- Kan, S.H., Metrics and models in software quality engineering. 2002: Addison-Wesley Longman Publishing Co., Inc.
- Dromey, G.R., A model for software product quality. Software Engineering, IEEE Transactions on, 1995. 21(2): p. 146-162.
- Al-Qutaish, R.E., Measuring the Software Product Quality during the Software Development Life-Cycle: An International Organization for Standardization Standards Perspective. Journal of Computer Science 5 (5), 2009.
- Yip, S.W. and T. Lam. A software maintenance survey. in Software Engineering Conference, 1994. Proceedings., 1994 First Asia-Pacific. 1994. IEEE.
- Bennett, K.H. and V.T. Rajlich. Software maintenance and evolution: a roadmap. in Proceedings of the Conference on the Future of Software Engineering. 2000. ACM.
- Grant, S., J.R. Cordy, and D.B. Skillicorn. Using topic models to support software maintenance. in Software Maintenance and Reengineering (CSMR), 2012 16th European Conference on. 2012. IEEE.
- Sjøberg, D.I., B. Anda, and A. Mockus. Questioning software maintenance metrics: a comparative case study. in Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement. 2012. ACM.
- Anquetil, N., et al., Software maintenance seen as a knowledge management issue. Information and Software Technology, 2007. 49(5): p. 515-529.
- Webster, K.P.B., K.M. de Oliveira, and N. Anquetil. A risk taxonomy proposal for software maintenance. in Software Maintenance, 2005. ICSM'05. Proceedings of the 21st IEEE International Conference on. 2005. IEEE.
- Rajlich, V. Software evolution and maintenance. in Proceedings of the on Future of Software Engineering. 2014. ACM.
- Bandini, S., Paoli, F. D., Manzoni, S., Mereghetti, P., A support system to COTS based software development for business services. Proceedings of the 14th International Conference on Software Engineering and Know ledge Engineering, Ischia, Italy, 2002. Vol. 27: p. 307–314.
- Kajko-Mattsson, M., et al., A model of maintainability–suggestion for future research. 2006.
- Aggarwal, K., et al., Measurement of software maintainability using a fuzzy model. Journal of Computer Science, 2005. 1(4): p. 538.
- Wai, R.-J. and Z.-W. Yang, Adaptive fuzzy neural network control design via a T–S fuzzy model for a robot manipulator including actuator dynamics. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 2008. 38(5): p. 1326-1346.
- Dit, B., et al. A dataset from change history to support evaluation of software maintenance tasks. in Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. 2013. IEEE.
- Chai, T. and R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific Model Development Discussions, 2014. 7: p. 1525-1534.
- Oseni, O.F., et al., Comparative Analysis of Received Signal Strength Prediction Models for Radio Network Planning of GSM 900 MHz in Ilorin, Nigeria. 2014.
- Ronquist, F., et al., MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic biology, 2012. 61(3): p. 539-542.
