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