Exploration of neural network models for defect detection and classification. (2016)

• Record Type: Journal Article
• Authors: Praskash, B.V. Ajay; Ashoka, D.V.; Aradhya, V.N. Manjunath; Naveena, C.
• Is Part Of: International journal of convergence computing. Volume 2: Number 3/4(2016)
• Page Start: 220
• Page End: 234
• Publication Date: 2016
• Subjects: software bugs tracking -- neural network models -- software quality assurance -- bug tracking -- defect classification; Computer science -- Periodicals; 004.05
• Languages: English
• ISSNs: 2048-9129

Abstract

In order to overcome the software development challenges like delivering a project on time, developing quality software products and reducing development cost, software industries commonly uses defect detection software tools to manage quality in software products. Defects are detected and classified based on their severity, this can be automated in order to reduce the development time and cost. Nowadays to extract useful knowledge from large software repositories, engineers and researchers are using data mining techniques. In this paper, software defect detection and classification method is proposed and neural network models such as generalised regression neural network (GRNN) and probabilistic neural network model (PNN) are integrated to identify, classify the defects from large software repository. Based on defects, severity proposed method discussed in this paper focuses on three layers: core, abstraction and application layer. The performance accuracy of the proposed model is compared with MLP and J48 classifiers.