Software Fault Prediction with Object-Oriented Metrics Based Artificial Immune Recognition System


Çatal Ç., DİRİ B.

8th International Product Focused Software and Process Improvement, Riga, Lithuania, 02 July 2007, pp.1-5

  • Publication Type: Conference Paper / Full Text
  • City: Riga
  • Country: Lithuania
  • Page Numbers: pp.1-5

Abstract

Abstract

Software testing is a time-consuming and expensive process. Software fault prediction models are used to identify fault-prone classes automatically before system testing. These models can reduce the testing duration, project risks, resource and infrastructure costs. In this study, we propose a novel fault prediction model to improve the testing process. Chidamber-Kemerer Object-Oriented metrics and method-level metrics such as Halstead and McCabe are used as independent metrics in our Artificial Immune Recognition System based model. According to this study, class-level metrics based model which applies AIRS algorithm can be used successfully for fault prediction and its performance is higher than J48 based approach. A fault prediction tool which uses this model can be easily integrated into the testing process.