Automation of systematic literature reviews: A systematic literature review

By Raymon van Dinter, Bedir Tekinerdogan, Cagatay Catal. Information and Software Technology, 2021

393 citations recorded by the source corpus.

A B S T R A C T 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.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/05491e1b16bfcb68022cde37442ffde6a5a3ccfd

Citations in the corpus

• Artificial intelligence and illusions of understanding in scientific research (2024, neutral): “Multiple AI tools that can query, digest and summarize the published record are being developed and evaluated 23,27,28 .”
• Comparing LLMs and Proposing an ML-Based Approach for Search String Generation in Systematic Literature Reviews (2025, unknown): “We chose to use this information instead of full text because, as re-ported by Van Dinter et al. [33], few automated approaches rely on full-text selection.”