Finding Better Active Learners for Faster Literature Reviews
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, combining and parametrizing the most efficient active learning algorithms. This paper assesses FASTREAD using datasets generated from existing SE literature reviews (Hall, Wahono, Radjenović, Kitchenham et al.). Compared to manual methods, FASTREAD lets researchers find 95% relevant studies after reviewing an order of magnitude fewer papers. Compared to other state-of-the-art automatic methods, FASTREAD reviews 20–50% fewer studies while finding same number of relevant primary studies in a systematic literature review.
Wed 7 Nov Times are displayed in time zone: (GMT-05:00) Guadalajara, Mexico City, Monterrey change
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Miltiadis AllamanisMicrosoft Research, Cambridge, Earl T. BarrUniversity College London, Christian BirdMicrosoft Research, Prem DevanbuUniversity of California, Mark MarronMicrosoft Research, Charles SuttonUniversity of EdinburghDOI
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