Finding Better Active Learners for Faster Literature Reviews
| Autor: | Zhe Yu, Nicholas A. Kraft, Tim Menzies |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Information retrieval D.2.0 Computer Science - Artificial Intelligence Computer science I.2.7 4. Education Cataloging 020207 software engineering 02 engineering and technology computer.software_genre Electronic discovery Software Engineering (cs.SE) Computer Science - Software Engineering Systematic review Artificial Intelligence (cs.AI) Code refactoring Active learning 0202 electrical engineering electronic engineering information engineering 68N01 68T50 020201 artificial intelligence & image processing computer Software |
| DOI: | 10.48550/arxiv.1612.03224 |
| 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: | OpenAIRE |
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