Wed 7 Nov 2018 15:30 - 15:52 at Horizons 6-9F - Mining Chair(s): Hridesh Rajan

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

15:30 - 17:00: Research Papers - Mining at Horizons 6-9F
Chair(s): Hridesh RajanIowa State University
fse-2018-Journal-First15:30 - 15:52
Zhe Yu, Nicholas A. KraftABB Corporate Research, Tim MenziesNorth Carolina State University
fse-2018-Journal-First15:52 - 16:15
Miltiadis AllamanisMicrosoft Research, Cambridge, Earl T. BarrUniversity College London, Christian BirdMicrosoft Research, Prem DevanbuUniversity of California, Mark MarronMicrosoft Research, Charles SuttonUniversity of Edinburgh
fse-2018-research-papers16:15 - 16:37
Pan BianRenmin University of China, China, Bin LiangRenmin University of China, China, Wenchang ShiRenmin University of China, China, Jianjun HuangRenmin University of China, China, Yan CaiInstitute of Software, Chinese Academy of Sciences
fse-2018-research-papers16:37 - 17:00
Daniel DeFreezUniversity of California, Davis, Aditya V. ThakurUniversity of California, Davis, Cindy Rubio-GonzálezUniversity of California, Davis