A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools. (May 2022)

• Record Type: Journal Article
• Authors: Pachouly, Jalaj; Ahirrao, Swati; Kotecha, Ketan; Selvachandran, Ganeshsree; Abraham, Ajith
• Is Part Of: Engineering applications of artificial intelligence. Volume 111(2022)
• Publication Date: 2022-05
• Subjects: Software defect prediction -- Classification -- Artificial intelligence -- Machine learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104773
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Delivering high-quality software products is a challenging task. It needs proper coordination from various teams in planning, execution, and testing. Many software products have high numbers of defects revealed in a production environment. Software failures are costly regarding money, time, and reputation for a business and even life-threatening if utilized in critical applications. Identifying and fixing software defects in the production system is costly, which could be a trivial task if detected before shipping the product. Binary classification is commonly used in existing software defect prediction studies. With the advancements in Artificial Intelligence techniques, there is a great potential to provide meaningful information to software development teams for producing quality software products. An extensive survey for Software Defect Prediction is necessary for exploring datasets, data validation methods, defect detection, and prediction approaches and tools. The survey infers standard datasets utilized in early studies lack adequate features and data validation techniques. According to the finding of the literature survey, the standard datasets has few labels, resulting in insufficient details regarding defects. Systematic Literature Reviews (SLR) on Software Defect Prediction are limited. Hence this SLR presents a comprehensive analysis of defect datasets, dataset validation, detection, prediction approaches, and tools for Software Defect Prediction. The survey exhibits the futuristic recommendations that will allow researchers to develop a tool for Software Defect Prediction. The survey introduces the architecture for developing a software prediction dataset with adequate features and statistical data validation techniques for multi-label classification for software defects.