Using Machine Learning for Systematic Literature Review Case in Point: Agile Software Development

Abstract

Systematic literature reviews (SLRs) are essential for researchers to keep up with past and recent research in their domains. However, the rapid growth in knowledge creation and the rising number of publications have made this task increasingly complex and challenging. Moreover, most systematic literature reviews are performed manually, which requires significant effort and creates potential bias. The risk of bias is particularly relevant in the data synthesis task, where researchers interpret each study's evidence and summarize the results. This study uses an experimental approach to explore using machine learning (ML) techniques in the SLR process. Specifically, this study replicates a study that manually performed sentiment analysis for the data synthesis step to determine the polarity (negative or positive) of evidence extracted from studies in the field of agile methodology. This study employs a lexicon‐based approach to sentiment analysis and achieves an accuracy rate of approximately 86.5% in identifying study evidence polarity.