# Automatic Bug Triage using Semi-Supervised Text Classification

arXiv: https://arxiv.org/abs/1704.04769 · HTML: https://mlsanity.com/paper/1704.04769/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2017.

## Built on by (most cited first, 6 of 6)

- [Past, Present, and Future of Bug Tracking in the Generative AI Era](https://mlsanity.com/paper/2510.08005/) (2025)
- [DABT: A Dependency-aware Bug Triaging Method](https://mlsanity.com/paper/2104.12744/) (2021)
- [S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems](https://mlsanity.com/paper/2204.05972/) (2022)
- [A Comparative Study of Transformer-based Neural Text Representation Techniques on Bug Triaging](https://mlsanity.com/paper/2310.06913/) (2023)
- [ADPTriage: Approximate Dynamic Programming for Bug Triage](https://mlsanity.com/paper/2211.00872/) (2022)
- [Comparison of Machine Learning Methods for Assigning Software Issues to Team Members](https://mlsanity.com/paper/2307.00009/) (2023)

## Abstract (arXiv)

In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectation-maximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the approach iteratively labels numerous unlabeled bug reports and trains a new classifier with labels of all the bug reports. We also employ a weighted recommendation list to boost the performance by imposing the weights of multiple developers in training the classifier. Experimental results on bug reports of Eclipse show that our new approach outperforms existing supervised approaches in terms of classification accuracy.