Nov7 '18 by Admin

Zhe Yu’s paper FAST2: an Intelligent Assistant for Finding Relevant Papers is accepted by Expert Systems with Applications.

Abstract: 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. FAST2 is a novel tool for assisting the researchers to find the next promising paper to read. This paper describes FAST2 and tests it on four large software engineering systematic literature review datasets. We find that FAST2 is a faster and robust tool to assist researcher finding relevant SE papers while also compensating for the errors made by humans during the review process. The effectiveness of FAST2 can be attributed to three key innovations:

  • 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;
  • 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;
  • a novel human error correction algorithm-which corrects a majority of human errors without imposing too much extra cost.