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

arXiv: https://arxiv.org/abs/2109.13825 · HTML: https://mlsanity.com/paper/2109.13825/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data](https://mlsanity.com/paper/2012.03468/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning to Prioritize IT Tickets: A Comparative Evaluation of Embedding-based Approaches and Fine-Tuned Transformer Models](https://mlsanity.com/paper/2512.17916/) (2025)

## Abstract (arXiv)

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.