A Machine Learning Approach for Semi-Automated Search and Selection in Literature Studies

Background. Search and selection of primary studies in Systematic Literature Reviews (SLR) is labour intensive, and hard to replicate and update. Aims. We explore a machine learning approach to support semi-automated search and selection in SLRs to address these weaknesses. Method. We 1) train a classi er on an initial set of papers, 2) extend this set of papers by automated search and snowballing, 3) have the researcher validate the top paper, selected by the classi er, and 4) update the set of papers and iterate the process until a stopping criterion is met. Results. We demonstrate with a proof-of-concept tool that the proposed automated search and selection approach generates valid search strings and that the performance for subsets of primary studies can reduce the manual work by half. Conclusions. Thee approach is promising and the demonstrated advantages include cost savings and replicability. e next steps include further tool development and evaluate the approach on a complete SLR.