R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction

R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction

Ekbal Rashid (Aurora's Technological and Research Institute, India)
DOI: 10.4018/IJSSCI.2016070102
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Abstract

Making R4 model effective and efficient I have introduced some new features, i.e., renovation of knowledgebase (KBS) and reducing the maintenance cost by removing the duplicate record from the KBS. Renovation of knowledgebase is the process of removing duplicate record stored in knowledgebase and adding world new problems along with world new solutions. This paper explores case-based reasoning and its applications for software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The system predicts the error level with respect to LOC and with respect to development time, and both affects the quality level. This paper also reviews four existing models of case-based reasoning (CBR). The paper presents a work in which I have expanded our previous work (Rashid et al., 2012). I have used different similarity measures to find the best method that increases reliability. The present work is also credited through introduction of some new terms like coefficient of efficiency, i.e., developer's ability.
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2. Overview Of Machine Learning

Machine learning deals with the problem of building computer programs that improve their performance at some task through experience (Michalski et al., 1998). Machine learning has been utilized in various problem domains. Some typical applications of machine learning are (Rashid et al., 2012):

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