2021/09/28 by Thi Hang, Do, Thi Thu Hang, Markus Dobler +3 · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering Research #Software Engineering Techniques and Practices #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2109.13825
openalex publication_date 2021/09/28 · openalex created_date 2021/10/11 · openalex updated_date 2026/07/28
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.