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

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.

Comparison of Machine Learning Techniques for Software Quality Prediction

Software quality prediction is one the most challenging tasks in the development and maintenance of software. Machine learning (ML) is widely being incorporated for the prediction of the quality of a final product in the early development stages of the software development life cycle (SDLC). An ML prediction model uses software metrics and faulty data from previous projects to detect high-risk modules for future projects, so that the testing efforts can be targeted to those specific ‘risky' modules. Hence, ML-based predictors contribute to the detection of development anomalies early and inexpensively and ensure the timely delivery of a successful, failure-free and supreme quality software product within budget. This article has a comparison of 30 software quality prediction models (5 technique * 6 dataset) built on five ML techniques: artificial neural network (ANN); support vector machine (SVMs); Decision Tree (DTs); k-Nearest Neighbor (KNN); and Naïve Bayes Classifiers (NBC), using six datasets: CM1, KC1, KC2, PC1, JM1, and a combined one. These models exploit the predictive power of static code metrics, McCabe complexity metrics, for quality prediction. All thirty predictors are compared using a receiver operator curve (ROC), area under the curve (AUC), and accuracy as performance evaluation criteria. The results show that the ANN technique for software quality prediction is promising for accurate quality prediction irrespective of the dataset used.

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

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.