Finding better active learners for faster literature reviews

Tim Menzies

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.
Original languageAmerican English
JournalEmpirical Software Engineering
StatePublished - Jan 1 2018


Dive into the research topics of 'Finding better active learners for faster literature reviews'. Together they form a unique fingerprint.

Cite this