- author = "Quentin Perez and Pierre-Antoine Jean and Christelle Urtado and Sylvain Vauttier",
- title = "Bug or not bug? That is the question",
- booktitle = "2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)",
- year = "2021",
- pages = "47--58",
- keywords = "genetic algorithms, genetic programming, SBSE, NLP, ANN, Bug Classification, Bug Tickets, Empirical Software Engineering, Natural Language Processing, Neural Network Optimisation",
-
DOI = "
doi:10.1109/ICPC52881.2021.00014",
- size = "12 pages",
- abstract = "Nowadays, development teams often rely on tools such as Jira or Bugzilla to manage back logs of issues to be solved to develop or maintain software. Although they relate to many different concerns (e.g., bug fixing, new feature development, architecture refactoring), few means are proposed to identify and classify these different kinds of issues, except for non mandatory labels that can be manually associated to them. This may lead to a lack of issue classification or to issue misclassification that may impact automatic issue management (planning, assignment)or issue-derived metrics. Automatic issue classification thus is a relevant topic for assisting backlog management. This paper proposes a binary classification solution for discriminating bug from non bug issues. This solution combines natural language processing (TF-IDF) and classification (multi-layer perceptron) techniques, selected after comparing commonly used solutions to classify issues. Moreover, hyper-parameters of the neural network are optimised using a genetic algorithm. The obtained results, as compared to existing works on a commonly used benchmark, show significant improvements on the F1 measure for all datasets.",
-
notes = "Is this GP?
HTTPClient, Jackrabbit, Lucene-Java, Rhino and Tomcat 5
EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France",
