Chapter

Agile Processes in Software Engineering and Extreme Programming

Volume 9 of the series Lecture Notes in Business Information Processing pp 215-217

Predicting Software Fault Proneness Model Using Neural Network

  • Yogesh SinghAffiliated withUniversity School of Information Technology, GGS Indraprastha University
  • , Arvinder KaurAffiliated withUniversity School of Information Technology, GGS Indraprastha University
  • , Ruchika MalhotraAffiliated withUniversity School of Information Technology, GGS Indraprastha University

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Abstract

Importance of quality software is increasing leading to development of sophisticated techniques for exploring data sets, which can be used in constructing models for predicting quality attributes. There have been few empirical studies evaluating the impact of object-oriented metrics on software quality and constructing models that utilize them in predicting quality attributes of the system. Most of these predicted models are built using statistical techniques. Most of these prediction models are built using statistical techniques. ANN have seen an explosion of interest over the years, and are being successfully applied across a range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. ANN can be used as a predictive model because it is very sophisticated modeling techniques capable of modeling complex functions.