Clustering Mobile Apps Based on Mined Textual Features
- Submitting institution
-
University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 122098
- Type
- E - Conference contribution
- DOI
-
10.1145/2961111.2962600
- Title of conference / published proceedings
- 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement - ESEM '16
- First page
- 38
- Volume
- -
- Issue
- -
- ISSN
- 19493770
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2016
- URL
-
https://doi.org/10.1145/2961111.2962600
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
A - Innovative Computing
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper's unsupervised clustering approach, which incorporates semantic relatedness of terms has highlighted the importance of semantic similarity when statically analyzing software textual artifacts. The paper's approach has also been applied in the industry during the first author's internship at an investment bank. The stakeholders agreed that applying the algorithm has significantly reduced the amount of manual work it would have required otherwise.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -