How to Read Less: On the Benefit of Active Learning for Primary Study Selection in Systematic Literature Reviews

Systematic literature reviews (SLRs) are the primary method for aggregating and synthesizing evidence in evidence-based software engineering (SE). Primary study selection is a critical and time-consuming SLR step in which reviewers use titles, abstracts, or even full texts to evaluate thousands of studies to find the dozens of them that are relevant to the research questions. We seek to reduce the effort of primary study selection in SE SLRs by exploring and refactoring the state-of-the-art active learning techniques from evidence-based medicine and legal electronic discovery. By refactoring those methods, we discovered FASTREAD, which is a new state-of-the-art in active learning for SE SLRs. When tested on four datasets generated from existing SE SLRs of Hall, Wahono, Radjenovic, Kitchenham et al., FASTREAD outperformed the current state-of-the-art methods. Our results show that FASTREAD can save researchers much time during the literature review process (since they will need to review hundreds to thousands fewer abstracts) while sacrificing very little in the final recall (5\%).