2026-06-05T21:44:56Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112832026-01-27T01:05:46Zcom_10259_4219com_10259_5086com_10259_2604com_10259_5377col_10259_4220col_10259_5378

Label prediction on issue tracking systems using text mining Alonso-Abad, Jesús M. López Nozal, Carlos Maudes Raedo, Jesús M. Marticorena Sánchez, Raúl Text classifier Experimentation in software engineering Issue tracker system Text mining Label prediction 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. 2026-01-26T08:41:56Z 2026-01-26T08:41:56Z 2019-09 info:eu-repo/semantics/article 2192-6352 https://hdl.handle.net/10259/11283 10.1007/s13748-019-00182-2 2192-6360 eng Progress in Artificial Intelligence. 2019, V. 8, n. 3, p. 325–342 https://doi.org/10.1007/s13748-019-00182-2 info:eu-repo/semantics/openAccess Springer