FAST$^2$: 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. 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), Radjenovi\'c (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 paperswhich helps to find relevant research papers faster with less variances; (2) an estimator of the number of remaining relevant papers yet to be foundwhich in practical settings can be used to decide if the reviewing process needs to be terminated; (3) a novel self-correcting classification algorithmautomatically corrects itself, in cases where the researcher wrongly classifies a paper.