Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2022

Abstract

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.

Keywords

Predictive models, machine learning, deep learning, software engineering, survey

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering; Cybersecurity; Intelligent Systems and Optimization; Software and Cyber-Physical Systems

Publication

ACM Transactions on Software Engineering and Methodology

Volume

31

Issue

3

First Page

1

Last Page

72

ISSN

1049-331X

Identifier

10.1145/3503509

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3503509

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