A survey on machine learning techniques used for software quality prediction. (2016)

• Record Type: Journal Article
• Authors: Pattnaik, Saumendra; Pattanayak, Binod Kumar
• Is Part Of: International journal of reasoning-based intelligent systems. Volume 8:Number 1/2(2016)
• Page Start: 3
• Page End: 14
• Publication Date: 2016
• Subjects: quality models -- quality attributes -- machine learning -- software quality prediction -- fuzzy logic -- software errors -- software development -- survey -- neural networks -- Bayesian modelling; Artificial intelligence -- Periodicals; Reasoning -- Periodicals; 006.3
• Languages: English
• ISSNs: 1755-0556

Abstract

In the present software development scenario, software quality prediction has become significantly important for successful implementation of the software in real world application and enhances the longevity of its functionality. Moreover, early identification of anticipated fault prone software modules in the process of development of software is crucial in saving efforts involved in this process. Machine learning techniques are considered to be the most appropriate techniques for software quality prediction and a large spectrum of research work has been conducted in this direction by several authors. In this paper, we conduct an extensive survey on various machine learning techniques like fuzzy logic, neural network, and Bayesian model, etc. used for software quality prediction along with an analytical justification for each of the proposed solutions.