Automation of systematic literature reviews: A systematic literature review

Resource type
Journal Article
Authors/contributors
Title
Automation of systematic literature reviews: A systematic literature review
Abstract
Context Systematic Literature Review (SLR) studies aim to identify relevant primary papers, extract the required data, analyze, and synthesize results to gain further and broader insight into the investigated domain. Multiple SLR studies have been conducted in several domains, such as software engineering, medicine, and pharmacy. Conducting an SLR is a time-consuming, laborious, and costly effort. As such, several researchers developed different techniques to automate the SLR process. However, a systematic overview of the current state-of-the-art in SLR automation seems to be lacking. Objective This study aims to collect and synthesize the studies that focus on the automation of SLR to pave the way for further research. Method A systematic literature review is conducted on published primary studies on the automation of SLR studies, in which 41 primary studies have been analyzed. Results This SLR identifies the objectives of automation studies, application domains, automated steps of the SLR, automation techniques, and challenges and solution directions. Conclusion According to our study, the leading automated step is the Selection of Primary Studies. Although many studies have provided automation approaches for systematic literature reviews, no study has been found to apply automation techniques in the planning and reporting phase. Further research is needed to support the automation of the other activities of the SLR process.
Publication
Information and Software Technology
Volume
136
Pages
106589
Date
2021-08-01
Journal Abbr
Information and Software Technology
ISSN
0950-5849
Short Title
Automation of systematic literature reviews
Accessed
18/01/2024, 21:13
Library Catalogue
ScienceDirect
Citation
van Dinter, R., Tekinerdogan, B., & Catal, C. (2021). Automation of systematic literature reviews: A systematic literature review. Information and Software Technology, 136, 106589. https://doi.org/10.1016/j.infsof.2021.106589