Authors: Saumendra Pattnaik; Binod Kumar Pattanayak
Addresses: Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Khandagiri, Odisha, India ' Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Khandagiri, Odisha, India
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.
Keywords: quality models; quality attributes; machine learning; software quality prediction; fuzzy logic; software errors; software development; survey; neural networks; Bayesian modelling.
International Journal of Reasoning-based Intelligent Systems, 2016 Vol.8 No.1/2, pp.3 - 14
Received: 07 Nov 2015
Accepted: 04 Jan 2016
Published online: 27 Oct 2016 *