A systematic review of fault prediction performance in software engineering

This is a post-print of the article accepted for publication. The definitive version can be accessed at the link below.Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs and improve the quality of software. Objective: We investigate how the context of models, the independent variables used and the modelling techniques applied, influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesise the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modelling techniques such as Naïve Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology and performance comprehensively.This research was supported by the UK's Engineering and Physical Science Research Council grant EPSRC EP/E063039/1 and the Science Foundation Ireland grant 3/CE2/I303