2019
Alonso-Abad, Jesús M.; López-Nozal, Carlos; Maudes-Raedo, Jesús; Marticorena-Sánchez, Raúl
Label prediction on issue tracking systems using text mining Journal Article
In: Progress in Artificial Intelligence, pp. 1-18, 2019, ISSN: 2192-6360.
Abstract | Links | BibTeX | Tags: Experimentation in software engineering, Issue tracker system, Label prediction, Text classifier, Text mining
@article{Alonso-Abad2019,
title = {Label prediction on issue tracking systems using text mining},
author = {Jesús M. Alonso-Abad and Carlos López-Nozal and Jesús Maudes-Raedo and Raúl Marticorena-Sánchez},
url = {https://link.springer.com/article/10.1007/s13748-019-00182-2?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst&utm_source=ArticleAuthorOnlineFirst&utm_medium=email&utm_content=AA_en_06082018&ArticleAuthorOnlineFirst_20190331},
doi = {10.1007/s13748-019-00182-2},
issn = {2192-6360},
year = {2019},
date = {2019-03-28},
journal = {Progress in Artificial Intelligence},
pages = {1-18},
abstract = {Issue tracking systems are overall change-management tools in software development. The issue-solving life cycle is a complex socio-technical activity that requires team discussion and knowledge sharing between members. In that process, issue classification facilitates an understanding of issues and their analysis. Issue tracking systems permit the tagging of issues with default labels (e.g., bug, enhancement) or with customized team labels (e.g., test failures, performance). However, a current problem is that many issues in open-source projects remain unlabeled. The aim of this paper is to improve maintenance tasks in development teams, evaluating models that can suggest a label for an issue using its text comments. We analyze data on issues from several GitHub trending projects, first by extracting issue information and then by applying text mining classifiers (i.e., support vector machine and naive Bayes multinomial). The results suggest that very suitable classifiers may be obtained to label the issues or, at least, to suggest the most suitable candidate labels.},
keywords = {Experimentation in software engineering, Issue tracker system, Label prediction, Text classifier, Text mining},
pubstate = {published},
tppubtype = {article}
}
Issue tracking systems are overall change-management tools in software development. The issue-solving life cycle is a complex socio-technical activity that requires team discussion and knowledge sharing between members. In that process, issue classification facilitates an understanding of issues and their analysis. Issue tracking systems permit the tagging of issues with default labels (e.g., bug, enhancement) or with customized team labels (e.g., test failures, performance). However, a current problem is that many issues in open-source projects remain unlabeled. The aim of this paper is to improve maintenance tasks in development teams, evaluating models that can suggest a label for an issue using its text comments. We analyze data on issues from several GitHub trending projects, first by extracting issue information and then by applying text mining classifiers (i.e., support vector machine and naive Bayes multinomial). The results suggest that very suitable classifiers may be obtained to label the issues or, at least, to suggest the most suitable candidate labels.