Comparative Study of Various Artificial Intelligence Techniques to Predict Software Quality

Software quality prediction models are used to identify software modules that may cause potential quality problems. These models are based on various metrics available during the early stages of software development life cycle like product size, software complexity, coupling and cohesion. In this survey paper, we have compared and discussed some software quality prediction approaches based on Bayesian belief network, neural networks, fuzzy logic, support vector machine, expectation maximum likelihood algorithm and case-based reasoning. This study gives better comparative insight about these approaches, and helps to select an approach based on available resources and desired level of quality.

[1]  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.

[2]  Taghi M. Khoshgoftaar,et al.  Return on investment of software quality predictions , 1998, Proceedings. 1998 IEEE Workshop on Application-Specific Software Engineering and Technology. ASSET-98 (Cat. No.98EX183).

[3]  Salah Bouktif,et al.  Improving Rule Set Based Software Quality Prediction: A Genetic Algorithm-based Approach , 2004, J. Object Technol..

[4]  Michael R. Lyu,et al.  A novel method for early software quality prediction based on support vector machine , 2005, 16th IEEE International Symposium on Software Reliability Engineering (ISSRE'05).

[5]  Tong-Seng Quah,et al.  Application of neural networks for software quality prediction using object-oriented metrics , 2003, International Conference on Software Maintenance, 2003. ICSM 2003. Proceedings..

[6]  Taghi M. Khoshgoftaar,et al.  Assessing uncertain predictions of software quality , 1999, Proceedings Sixth International Software Metrics Symposium (Cat. No.PR00403).

[7]  Jeffrey Voas.  Can Chaotic Methods Improve Software Quality Predictions? , 2000, IEEE Software.

[8]  Bo Yu,et al.  Extract rules from software quality prediction model based on neural network , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[9]  Houari Sahraoui,et al.  Building Quality Estimation models with Fuzzy Threshold Values , 2001 .

[10]  Michael R. Lyu,et al.  Software quality prediction using mixture models with EM algorithm , 2000, Proceedings First Asia-Pacific Conference on Quality Software.

[11]  Sousuke Amasaki,et al.  A Bayesian belief network for assessing the likelihood of fault content , 2003, 14th International Symposium on Software Reliability Engineering, 2003. ISSRE 2003..

[12]  Taghi M. Khoshgoftaar,et al.  Process measures for predicting software quality , 1997, Proceedings 1997 High-Assurance Engineering Workshop.

[13]  Taghi M. Khoshgoftaar,et al.  An empirical study of predicting software faults with case-based reasoning , 2006, Software Quality Journal.

[14]  R. Halstead,et al.  Using Process History to Predict Software Quality , 1998, Computer.

[15]  Houari A. Sahraoui,et al.  A fuzzy logic framework to improve the performance and interpretation of rule-based quality prediction models for OO software , 2002, Proceedings 26th Annual International Computer Software and Applications.

[16]  Jonathan Lee,et al.  A note on current approaches to extending software engineering with fuzzy logic , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[17]  Qinbao Song,et al.  Software defect association mining and defect correction effort prediction , 2006, IEEE Transactions on Software Engineering.

[18]  Thomas Liedtke,et al.  Comparison of conventional approaches and soft-computing approaches for software quality prediction , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[19]  Taghi M. Khoshgoftaar,et al.  Tree-based software quality estimation models for fault prediction , 2002, Proceedings Eighth IEEE Symposium on Software Metrics.

[20]  W. Pedrycz,et al.  Software quality prediction using median-adjusted class labels , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).