Position Paper: Defect Prediction Approaches for Software Projects Using Genetic Fuzzy Data Mining

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


Despite significant advances in software engineering research, the ability to produce reliable software products for a variety of critical applications remains an open problem. The key challenge has been the fact that each software product is unique, and existing methods are predominantly not capable of adapting to the observations made during project development. This paper makes the following claim: Genetic fuzzy data mining methods provide an ideal research paradigm for achieving reliable and efficient software defect pattern analysis. A brief outline of some fuzzy data mining methods is provided, along with a justification of why they are applicable to software defect analysis. Furthermore, some practical challenges to the extensive use of fuzzy data mining methods are discussed, along with possible solutions to these challenges.


Data Mining Fuzzy Clustering Software Engineering Random forest Metrics Software Quality Project Management 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dept. of CSEBIETDavanagereIndia
  2. 2.RIIC, Dayanada Sagar InstituteJain University, BangaloreBangaloreIndia
  3. 3.Dept. of CSE, RIICDayanada Sagar InstituteBangaloreIndia

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