Software Fault Prediction Models for Web Applications

Our daily life increasingly relies on Web applications. Web applications provide us with abundant services to support our everyday activities. As a result, quality assurance for Web applications is becoming important and has gained much attention from software engineering community. In recent years, in order to enhance software quality, many software fault prediction models have been constructed to predict which software modules are likely to be faulty during operations. Such models can be utilized to raise the effectiveness of software testing activities and reduce project risks. Although current fault prediction models can be applied to predict faulty modules of Web applications, one limitation of them is that they do not consider particular characteristics of Web applications. In this paper, we try to build fault prediction models aiming for Web applications after analyzing major characteristics which may impact on their quality. The experimental study shows that our approach achieves very promising results.

[1]  Daniel M. Brandon,et al.  Software Engineering for Modern Web Applications: Methodologies and Technologies , 2008 .

[2]  Paolo Tonella,et al.  Detecting anomaly and failure in Web applications , 2006, IEEE Multimedia.

[3]  Tim Menzies,et al.  Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.

[4]  Witold Pedrycz,et al.  A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[5]  Daniel T. Larose,et al.  Data mining methods and models , 2006 .

[6]  David L. Olson,et al.  Advanced Data Mining Techniques , 2008 .

[7]  Ye Wu,et al.  Modeling and Testing Web-based Applications , 2002 .

[8]  Lionel C. Briand,et al.  A systematic and comprehensive investigation of methods to build and evaluate fault prediction models , 2010, J. Syst. Softw..

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

[10]  Yuming Zhou,et al.  Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults , 2006, IEEE Transactions on Software Engineering.

[11]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[12]  Banu Diri,et al.  A systematic review of software fault prediction studies , 2009, Expert Syst. Appl..

[13]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[14]  Claes Wohlin,et al.  Experimentation in software engineering: an introduction , 2000 .

[15]  Zhao Li,et al.  Multi-faceted quality and defect measurement for web software and source contents , 2010, J. Syst. Softw..

[16]  Ala Abu-Samaha,et al.  Eliciting Web application requirements - an industrial case study , 2007, J. Syst. Softw..

[17]  Harvey P. Siy,et al.  Predicting Fault Incidence Using Software Change History , 2000, IEEE Trans. Software Eng..

[18]  Peter Dolog,et al.  Engineering Web Applications , 2009, Data-Centric Systems and Applications.

[19]  Brendan Murphy,et al.  Using Historical In-Process and Product Metrics for Early Estimation of Software Failures , 2006, 2006 17th International Symposium on Software Reliability Engineering.

[20]  Karim O. Elish,et al.  Predicting defect-prone software modules using support vector machines , 2008, J. Syst. Softw..

[21]  Nathan A. Curtis,et al.  Modular Web Design: Creating Reusable Components for User Experience Design and Documentation , 2009 .

[22]  Li-Wei Chen,et al.  Accuracy and efficiency comparisons of single- and multi-cycled software classification models , 2009, Inf. Softw. Technol..

[23]  Yuming Zhou,et al.  On the ability of complexity metrics to predict fault-prone classes in object-oriented systems , 2010, J. Syst. Softw..