Predictive Models in Software Engineering: Challenges and Opportunities
It addresses the need for a structured overview of predictive modeling in software engineering, but is incremental as it synthesizes existing studies without introducing new methods.
This paper systematically surveys 139 papers on predictive models in software engineering to organize knowledge, describing key models, classifying them, summarizing application areas, and analyzing results, while proposing challenges and a research roadmap for future work.
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-preformed studies and well-desigeworks in various research domains, including software requirements, software design and development, testing and debugging and software maintenance. This paper is a first attempt to systematically organize knowledge in this area by surveying a body of 139 papers on predictive models. 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.