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Computer Science > Machine Learning

arXiv:2109.13825 (cs)
[Submitted on 28 Sep 2021]

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

Authors:Thi Thu Hang Do, Markus Dobler, Niklas Kühl
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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.
Comments: Hawaii International Conference on System Sciences 2022 (HICSS-55)
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.13825 [cs.LG]
  (or arXiv:2109.13825v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.13825

Submission history

From: Niklas Kühl Dr [view email]
[v1] Tue, 28 Sep 2021 15:55:10 UTC (698 KB)
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