Soft Computing Approaches for Prediction of Software Maintenance Effort

The relationship between object oriented metrics and software maintenance effort is complex and non-linear. Therefore, there is considerable research interest in development and application of sophisticated techniques which can be used to construct models for predicting software maintenance effort. The aim of this paper is to evaluate and compare the application of different soft computing techniques – Artificial Neural Networks, Fuzzy Inference Systems and Adaptive Neuro-Fuzzy Inference Systems to construct models for prediction of Software Maintenance Effort. The maintenance effort data of two commercial software products is used in this study. The dependent variable in our study is maintenance effort. The independent variables are eight Object Oriented metrics . It is observed that soft computing techniques can be used for constructing accurate models for prediction of software maintenance effort and Adaptive Neuro Fuzzy Inference System technique gives the most accurate model.

[1]  Victor R. Basili,et al.  A Validation of Object-Oriented Design Metrics as Quality Indicators , 1996, IEEE Trans. Software Eng..

[2]  G. R. Finnie,et al.  AI tools for software development effort estimation , 1996, Proceedings 1996 International Conference Software Engineering: Education and Practice.

[3]  Arvinder Kaur,et al.  Application of Artificial Neural Network for Predicting Maintainability Using Object-Oriented Metrics , 2008 .

[4]  Taghi M. Khoshgoftaar,et al.  Can neural networks be easily interpreted in software cost estimation? , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[5]  Tong-Seng Quah,et al.  Application of neural network for predicting software development faults using object-oriented design metrics , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[6]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[7]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[8]  Stephen L. Chiu,et al.  Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification , 2000 .

[9]  Taghi M. Khoshgoftaar,et al.  An application of fuzzy clustering to software quality prediction , 2000, Proceedings 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology.

[10]  Chris F. Kemerer,et al.  A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..

[11]  Mei-Hwa Chen,et al.  Prediction of Software Readiness Using Neural Network , .

[12]  K. K. Aggarwal,et al.  Measurement of Software Maintainability Using a Fuzzy Model , 2005 .

[13]  Didier Dubois,et al.  Fuzzy information engineering: a guided tour of applications , 1997 .

[14]  Sallie M. Henry,et al.  Object-oriented metrics that predict maintainability , 1993, J. Syst. Softw..

[15]  Tibor Gyimóthy,et al.  Empirical validation of object-oriented metrics on open source software for fault prediction , 2005, IEEE Transactions on Software Engineering.

[16]  S. Sumathi,et al.  Introduction to neural networks using MATLAB 6.0 , 2006 .

[17]  Tong-Seng Quah,et al.  Application of Neural Networks for Estimating Software Maintainability Using Object-Oriented Metrics , 2003, SEKE.

[18]  Chris F. Kemerer,et al.  Towards a metrics suite for object oriented design , 2017, OOPSLA '91.