ABSTRACT
Software maintenance is an important aspect of software life cycle development, hence prior estimation of effort for maintainability plays a vital role. Existing approaches for maintainability estimation are mostly based on regression analysis and neural network approaches. It is observed that numerous software metrics are even used as input for estimation. In this study, Object-Oriented software metrics are considered to provide requisite input data for designing a model. It helps in estimating the maintainability of Object-Oriented software. Models for estimating maintainability are designed using the parallel computing concept of Neuro-Genetic algorithm (hybrid approach of neural network and genetic algorithm). This technique is employed to estimate the software maintainability of two case studies such as the User Interface System (UIMS), and Quality Evaluation System (QUES). This paper also focuses on the effectiveness of feature reduction techniques such as rough set analysis (RSA) and principal component analysis (PCA). The results show that, RSA and PCA obtained better results for UIMS and QUES respectively. Further, it observed the parallel computing concept is helpful in accelerating the training procedure of the neural network model.
- F. B. E. Abreu and R. Carapuca. Object-Oriented software engineering: Measuring and controlling the development process. In Proceedings of the 4th International Conference on Software Quality, volume 186, 1994.Google Scholar
- R. K. Bandi, V. K. Vaishnavi, and D. E. Turk. Predicting maintenance performance using object-oriented design complexity metrics. IEEE Transactions on Software Engineering, 29(1):77--87, 2003. Google ScholarDigital Library
- A. B. Binkley and S. R. Schach. Validation of the coupling dependency metric as a predictor of run-time failures and maintenance measures. In Proceedings of the 20th international conference on Software engineering, pages 452--455. IEEE Computer Society, 1998. Google ScholarDigital Library
- L. C. Briand, J. Wüst, J. W. Daly, and D. V. Porter. Exploring the relationships between design measures and software quality in Object-Oriented systems. The Journal of Systems and Software, 51(3):245--273, May 2000. Google ScholarDigital Library
- C. Burgess and M.Lefley. Can genetic programming improve software effort estimation. Information and Software Technology, 43:863--873, 2001.Google ScholarCross Ref
- J.-C. Chen and S.-J. Huang. An empirical analysis of the impact of software development problem factors on software maintainability. Journal of Systems and Software, 82(6):981--992, 2009. Google ScholarDigital Library
- S. R. Chidamber and C. F. Kemerer. A metrics suite for Object-Oriented design. IEEE Transactions on Software Engineering, 20(6):476--493, June 1994. Google ScholarDigital Library
- D. Coleman, D. Ash, B. Lowther, and P. Oman. Using metrics to evaluate software system maintainability. IEEE Computer, 27(8):44--49, 1994. Google ScholarDigital Library
- D. Coleman, B. Lowther, and P. Oman. The application of software maintainability models in industrial software systems. Journal of Systems and Software, 29(1):3--16, 1995. Google ScholarDigital Library
- R. Gu, F. Shen, and Y. Huang. A parallel computing platform for training large scale neural networks. In 2013 IEEE International Conference on Big Data, pages 376--384, 2013.Google ScholarCross Ref
- M. Halstead. Elements of Software Sciencel. Elsevier Science, New York, USA, 1977. Google ScholarDigital Library
- B. Henderson-Sellers. Software Metrics. Prentice-Hall, UK, 1996.Google Scholar
- H.-W. Jung, S.-G. Kim, and C.-S. Chung. Measuring software product quality: A survey of iso/iec 9126. IEEE software, 21(5):88--92, 2004. Google ScholarDigital Library
- B. K. Kang and J. M. Bieman. Cohesion and reuse in an Object-Oriented system. In Proceedings of the ACM SIGSOFT Symposium on software reuseability, pages 259--262. Seattle, March 1995. Google ScholarDigital Library
- J. Kaur, S. Singh, K. S. Kahlon, and P. Bassi. Neural network-a novel technique for software effort estimation. International Journal of Computer Theory and Engineering, 2(1):17--19, 2010.Google ScholarCross Ref
- Kim and Ji-Hyun. Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics and Data Analysis, 53(11):3735--3745, 2009. Google ScholarDigital Library
- R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, San Mateo, pages 1137--1143, 1995. Google ScholarDigital Library
- A. Lake and C. Cook. Use of factor analysis to develop oop software complexity metrics. In Proceedings of 6th Annual Oregon Workshop on Software Metrics, Silver Falls, Oregon, 1994.Google Scholar
- W. Li and S. Henry. Maintenance metrics for the Object-Oriented paradigm. In Proceedings of First International Software Metrics Symposium, pages 52--60, 1993.Google ScholarCross Ref
- M. Lorenz and J. Kidd. Object-Oriented Software Metrics. Prentice-Hall, NJ, Englewood, 1994. Google ScholarDigital Library
- T. J. McCabe. A complexity measure. IEEE Transactions on Software Engineering, 2(4):308--320, December 1976. Google ScholarDigital Library
- T. Menzies, B. Caglayan, Z. He, E. Kocaguneli, J. Krall, F. Peters, and B. Turhan. The promise repository of empirical software engineering data, June 2012.Google Scholar
- T. Menzies, Z. Chen, J. Hihn, and K. Lum. Selecting best practices for effort estimation. IEEE Transactions on Software Engineering, 32(11):883--895, 2006. Google ScholarDigital Library
- P. Oman and J. Hagemeister. Construction and testing of polynomials predicting software maintainability. Journal of Systems and Software, 24(3):251--266, 1994. Google ScholarDigital Library
- Z. Pawlak. Rough sets. International Journal of Computer and Information Sciences, 11(5):341--356, 1982.Google ScholarCross Ref
- S. L. Schneberger. Distributed computing environments: effects on software maintenance difficulty. Journal of Systems and Software, 37(2):101--116, 1997. Google ScholarDigital Library
- C. Van Koten and A. Gray. An application of bayesian network for predicting object-oriented software maintainability. Information and Software Technology, 48(1):59--67, 2006. Google ScholarDigital Library
- Y. Zhou and H. Leung. Predicting object-oriented software maintainability using multivariate adaptive regression splines. Journal of Systems and Software, 80(8):1349--1361, 2007. Google ScholarDigital Library
Index Terms
- Predicting Object-Oriented Software Maintainability using Hybrid Neural Network with Parallel Computing Concept
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