Predictive models in software engineering: Challenges and opportunities

• Main Authors: YANG, Yanming, XIA, Xin, LO, David, BI, Tingting, GRUNDY, John C., YANG, Xiaohu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Predictive models; machine learning; deep learning; software engineering; survey; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7630; https://ink.library.smu.edu.sg/context/sis_research/article/8633/viewcontent/2008.03656.pdf
• Institution: Singapore Management University

Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging, and software maintenance. This article is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application areas, and analyze research results. Based on our findings, we also propose a set of current challenges that still need to be addressed in future work and provide a proposed research road map for these opportunities.