FAST${}^2$: an Intelligent Assistant for Finding Relevant Papers

Abstract

Literature reviews are essential for any researcher trying to keep up to date with the burgeoning software engineering literature. FAST${}^2$ is a novel tool for reducing the effort required for conducting literature reviews by assisting the researchers to find the next promising paper to read (among a set of unread papers). This paper describes FAST${}^2$ and tests it on four large software engineering literature reviews conducted by Wahono (2015), Hall (2012), Radjenović (2013) and Kitchenham (2017). We find that FAST${}^2$ is a faster and robust tool to assist researcher finding relevant SE papers which can compensate 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 in practical settings can be used to decide if the reviewing process needs to be terminated; (3) a novel self-correcting classification algorithm---automatically corrects itself, in cases where the researcher wrongly classifies a paper.

20+3 pages, 6 figures, 5 tables, and 4 algorithms. Accepted by Journal of Expert Systems with Applications

References in corpus (2)

• Finding Better Active Learners for Faster Literature Reviews
• Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review

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