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.
- Machine learning
- Natural language processing
- Systematic literature review (SLR)
- Text mining