Predict Fault-Proneness Module Using Pattern Recognization (A Review)
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 4
Abstract
Abstract: 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 of this paper is We investigate how the context of models, the independent variables used and the modeling techniques applied, influence the performance of fault prediction models. Method on We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2012. 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. 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
Authors and Affiliations
Puneet Chopra, , Er. Gurdeep Kaur
Keywords
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