What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development

dc.contributor.author Do, Thi Thu Hang
dc.contributor.author Dobler, Markus
dc.contributor.author Kühl, Niklas
dc.date.accessioned 2021-12-24T17:23:20Z
dc.date.available 2021-12-24T17:23:20Z
dc.date.issued 2022-01-04
dc.description.abstract Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs. In this paper, we present an approach to predict the time to fix, the risk and the complexity of debugging and resolution of a bug report using different supervised machine learning algorithms namely Random Forest, Naive Bayes, SVM, MLP and XGBoost. Further, we investigate the effect of the application of active learning and we evaluate the impact of different text representation techniques, namely TF-IDF, Word2Vec, Universal Sentence Encoder and XLNet on the model's performance. The evaluation shows that a combination of text embeddings generated through the Universal Sentence Encoder and MLP as classifier outperforms all other methods, and is well suited to predict the risk and complexity of bug tickets.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.099
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79429
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Text Analytics
dc.subject artificial intelligence
dc.subject bug triaging
dc.subject hardware development
dc.subject machine learning
dc.subject natural language processing
dc.title What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development
dc.type.dcmi text
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