# DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging

arXiv: https://arxiv.org/abs/1801.01275 · HTML: https://mlsanity.com/paper/1801.01275/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2018.

## Built on by (most cited first, 20 of 20)

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- [A Comparative Study of Transformer-based Neural Text Representation Techniques on Bug Triaging](https://mlsanity.com/paper/2310.06913/) (2023)
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- [ADPTriage: Approximate Dynamic Programming for Bug Triage](https://mlsanity.com/paper/2211.00872/) (2022)
- [Is Hyper-Parameter Optimization Different for Software Analytics?](https://mlsanity.com/paper/2401.09622/) (2024)
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- [DapStep: Deep Assignee Prediction for Stack Trace Error rePresentation](https://mlsanity.com/paper/2201.05256/) (2022)
- [Comparison of Machine Learning Methods for Assigning Software Issues to Team Members](https://mlsanity.com/paper/2307.00009/) (2023)
- [DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce](https://mlsanity.com/paper/2307.11344/) (2023)
- [TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings](https://mlsanity.com/paper/2508.16860/) (2025)
- [Progressive Code Integration for Abstractive Bug Report Summarization](https://mlsanity.com/paper/2512.00325/) (2025)
- [LIA: Supervised Fine-Tuning of Large Language Models for Automatic Issue Assignment](https://mlsanity.com/paper/2601.01780/) (2026)

## Abstract (arXiv)

For a given software bug report, identifying an appropriate developer who could potentially fix the bug is the primary task of a bug triaging process. A bug title (summary) and a detailed description is present in most of the bug tracking systems. Automatic bug triaging algorithm can be formulated as a classification problem, with the bug title and description as the input, mapping it to one of the available developers (classes). The major challenge is that the bug description usually contains a combination of free unstructured text, code snippets, and stack trace making the input data noisy. The existing bag-of-words (BOW) feature models do not consider the syntactical and sequential word information available in the unstructured text. We propose a novel bug report representation algorithm using an attention based deep bidirectional recurrent neural network (DBRNN-A) model that learns a syntactic and semantic feature from long word sequences in an unsupervised manner. Instead of BOW features, the DBRNN-A based bug representation is then used for training the classifier. Using an attention mechanism enables the model to learn the context representation over a long word sequence, as in a bug report. To provide a large amount of data to learn the feature learning model, the unfixed bug reports (~70% bugs in an open source bug tracking system) are leveraged, which were completely ignored in the previous studies. Another contribution is to make this research reproducible by making the source code available and creating a public benchmark dataset of bug reports from three open source bug tracking system: Google Chromium (383,104 bug reports), Mozilla Core (314,388 bug reports), and Mozilla Firefox (162,307 bug reports). Experimentally we compare our approach with BOW model and machine learning approaches and observe that DBRNN-A provides a higher rank-10 average accuracy.