FAST2: An intelligent assistant for finding relevant papers

Literature reviews are essential for any researcher trying to keep up to date with the burgeoning software engineering literature. Finding relevant papers can be hard due to the huge amount of candidates provided by search. FAST(2) is a novel tool for assisting the researchers to find the next promising paper to read. This paper describes FAST(2) and tests it on four large systematic literature review datasets. We show that FAST(2) robustly optimizes the human effort to find most (95%) of the relevant software engineering papers while also compensating for the errors made by humans during the review process. The effectiveness of FAST(2) can be attributed to three key innovations: (1) a novel way of applying external domain knowledge (a simple two or three keyword search) to guide the initial selection of papers-which helps to find relevant research papers faster with less variances; (2) an estimator of the number of remaining relevant papers yet to be found-which helps the reviewer decide when to stop the review; (3) a novel human error correction algorithm-which corrects a majority of human misclassifications (labeling relevant papers as non-relevant or vice versa) without imposing too much extra human effort. (C) 2018 Elsevier Ltd. All rights reserved.