Back to Search
Start Over
FAST2: An intelligent assistant for finding relevant papers
- Source :
- Expert Systems with Applications. 120:57-71
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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.<br />Comment: 20+3 pages, 6 figures, 5 tables, and 4 algorithms. Accepted by Journal of Expert Systems with Applications
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
Information retrieval
D.2.0
Computer science
I.2.7
Human error
General Engineering
02 engineering and technology
Computer Science Applications
Software Engineering (cs.SE)
Computer Science - Software Engineering
020901 industrial engineering & automation
Systematic review
Artificial Intelligence
68N01, 68T50
0202 electrical engineering, electronic engineering, information engineering
Key (cryptography)
Selection (linguistics)
Domain knowledge
020201 artificial intelligence & image processing
Review process
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 120
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi.dedup.....313b227e38a0710095a989641f2081c8