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Label prediction on issue tracking systems using text mining

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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.

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We would like to thank the Ministerio de Economía y Competitividad of the Spanish Government for financing the Project TIN2015-67534-P (MINECO/FEDER, UE) and the Junta de Castilla y León for financing the Project BU085P17 (JCyL/FEDER, UE) both co-financed from European Union European Regional Development Fund (ERDF/FEDER) funds. We gratefully acknowledge the support of NVIDIA Corporation for the donation of TITAN Xp GPUs used for this research.

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See Tables 4 and 5.

Table 4 Methods and configurations that predict the best AUROC for each label
Table 5 Methods and configurations that predict the best optimized \(F_1\) score for each label

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Alonso-Abad, J.M., López-Nozal, C., Maudes-Raedo, J.M. et al. Label prediction on issue tracking systems using text mining. Prog Artif Intell 8, 325–342 (2019).

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