Title: Towards recent developments in the methods, metrics and datasets of software fault prediction
Authors: Deepak Sharma; Pravin Chandra
Addresses: University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Sector – 16 C, Dwarka, New Delhi 110078, India ' University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Sector – 16 C, Dwarka, New Delhi 110078, India
Abstract: The world of software systems is amplified with the changing environment magnifying the demand for quality software. Software fault prediction is a requisite activity ensuring the development of economic, efficient and quality software. It is the procedure for the development of models which help to identify faults in modules during early phases of software development lifecycle. Software fault prediction is one of the most prevalent research disciplines. The existing study in this domain includes numerous modelling techniques and software metrics for the early predictions of software faults. This paper aims to explore some of the prominent studies for software fault prediction in the existing literature. In this paper, software fault prediction papers since 1990 to 2017 are investigated. The paper includes the analysis of the studies having empirical validation and a good source of publication. The paper reflects the methods, metrics, and datasets available in the literature for software fault prediction. In addition, the modelling techniques based on traditional and computational intelligence-based methods are also reviewed. This paper is an endeavour to assemble the existing techniques and metrics of software fault prediction with a motive to assist researchers for easy evaluation of suitable metrics for their own research scenarios.
Keywords: software fault prediction; fault tolerance; computational intelligence; software metrics; evaluation metrics.
International Journal of Computational Systems Engineering, 2020 Vol.6 No.1, pp.14 - 45
Received: 20 Jan 2018
Accepted: 31 Mar 2018
Published online: 13 Aug 2020 *