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

## TL;DR

FAST$^2$ is an innovative AI-powered tool that significantly accelerates and improves the accuracy of literature reviews in software engineering by guiding initial paper selection, estimating remaining relevant papers, and self-correcting classifications.

## Contribution

The paper introduces FAST$^2$, a novel tool with three key innovations: external domain knowledge application, an estimator for remaining relevant papers, and a self-correcting classification algorithm.

## Key findings

- FAST$^2$ speeds up literature review process.
- It compensates for human errors in paper classification.
- FAST$^2$ is robust across multiple large reviews.

## 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), 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 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.