Finding Better Active Learners for Faster Literature Reviews

Autor:	Yu, Zhe, Kraft, Nicholas A., Menzies, Tim
Rok vydání:	2016
Předmět:	Computer Science - Software Engineering Computer Science - Artificial Intelligence 68N01 68T50 D.2.0 I.2.7
Druh dokumentu:	Working Paper
DOI:	10.1007/s10664-017-9587-0
Popis:	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, Radjenovi\'c, 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. Comment: 23 pages, 5 figures, 3 tables, accepted for publication in EMSE journal
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1612.03224 Zobrazit plný text záznamu View this record from Arxiv