Machine learning based methods for software fault prediction: A survey. (15th June 2021)

• Record Type: Journal Article
• Authors: Pandey, Sushant Kumar; Mishra, Ravi Bhushan; Tripathi, Anil Kumar
• Is Part Of: Expert systems with applications. Volume 172(2021)
• Publication Date: 2021-06-15
• Subjects: Machine learning -- Fault proneness -- Statistical techniques -- Fault prediction -- Systematic literature review; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114595
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The article is survey article. Survey of Software fault prediction techniques by various machine learning methods. It consist survey of 154 articles from 1990 to June 2019. Framed seven research queries and addressing each research query. Abstract: Several prediction approaches are contained in the arena of software engineering such as prediction of effort, security, quality, fault, cost, and re-usability. All these prediction approaches are still in the rudimentary phase. Experiments and research are conducting to build a robust model. Software Fault Prediction (SFP) is the process to develop the model which can be utilized by software practitioners to detect faulty classes/module before the testing phase. Prediction of defective modules before the testing phase will help the software development team leader to allocate resources more optimally and it reduces the testing effort. In this article, we present a Systematic Literature Review (SLR) of various studies from 1990 to June 2019 towards applying machine learning and statistical method over software fault prediction. We have cited 208 research articles, in which we studied 154 relevant articles. We investigated the competence of machine learning in existing datasets and research projects. To the best of our knowledge, the existing SLR considered only a few parameters over SFP's performance, and they partially examined the various threats and challenges of SFP techniques. In this article, we aggregated those parameters and analyzed them accordingly, and we also illustrate the different challenges in the SFP domain. We also compared the performance between machine learning and statistical techniques based on SFP models. Our empirical study and analysis demonstrate that the prediction ability of machine learning techniques for classifying class/module as fault/non-fault prone is better than classical statistical models. The performance of machine learning-based SFP methods over fault susceptibility is better than conventional statistical purposes. The empirical evidence of our survey reports that the machine learning techniques have the capability, which can be used to identify fault proneness, and able to form well-generalized result. We have also investigated a few challenges in fault prediction discipline, i.e., quality of data, over-fitting of models, and class imbalance problem. We have also summarized 154 articles in a tabular form for quick identification.