Finding Better Active Learners for Faster Literature Reviews

12/10/2016

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by   Zhe Yu, et al.

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Literature reviews can be time-consuming and tedious to complete. By cataloging and refactoring three state-of-the-art active learning techniques from evidence-based medicine and legal electronic discovery, this paper finds and implements FASTREAD, a faster technique for studying a large corpus of documents. This paper assesses FASTREAD using datasets generated from existing SE literature reviews (Hall, Wahono, Radjenovic, Kitchenham et al.). Compared to manual methods, FASTREAD lets researchers find 95 reviewing an order of magnitude fewer papers. Compared to other state-of-the-art automatic methods, FASTREAD reviews 20-50 finding same number of relevant primary studies in a systematic literature review.