# LIA: Supervised Fine-Tuning of Large Language Models for Automatic Issue Assignment

arXiv: https://arxiv.org/abs/2601.01780 · HTML: https://mlsanity.com/paper/2601.01780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging](https://mlsanity.com/paper/1801.01275/) (2018)
- [Topic Recommendation for Software Repositories using Multi-label Classification Algorithms](https://mlsanity.com/paper/2010.09116/) (2020)
- [Predicting the Objective and Priority of Issue Reports in Software Repositories](https://mlsanity.com/paper/2012.10951/) (2020)
- [EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery](https://mlsanity.com/paper/2308.10759/) (2023)
- [An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation](https://mlsanity.com/paper/2408.09078/) (2024)
- [Back to the Basics: Rethinking Issue-Commit Linking with LLM-Assisted Retrieval](https://mlsanity.com/paper/2507.09199/) (2025)
- [Towards Effective Bug Triage with Towards Effective Bug Triage with Software Data Reduction Techniques](https://mlsanity.com/paper/1704.04761/) (2017)
- [A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging](https://mlsanity.com/paper/2101.11846/) (2021)
- [Factoring Expertise, Workload, and Turnover into Code Review Recommendation](https://mlsanity.com/paper/2312.17236/) (2023)
- [Can GitHub Issues Help in App Review Classifications?](https://mlsanity.com/paper/2308.14211/) (2023)
- [A Comparative Study of Transformer-based Neural Text Representation Techniques on Bug Triaging](https://mlsanity.com/paper/2310.06913/) (2023)
- [GIRT-Model: Automated Generation of Issue Report Templates](https://mlsanity.com/paper/2402.02632/) (2024)
- [LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery](https://mlsanity.com/paper/2508.12232/) (2025)
- [IssueCourier: Multi-Relational Heterogeneous Temporal Graph Neural Network for Open-Source Issue Assignment](https://mlsanity.com/paper/2505.11205/) (2025)
- [Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models](https://mlsanity.com/paper/2506.00128/) (2025)
- [Hybrid LLM Routing for Efficient App Feedback Classification](https://mlsanity.com/paper/2507.08250/) (2025)

## Abstract (arXiv)

Issue assignment is a critical process in software maintenance, where new issue reports are validated and assigned to suitable developers. However, manual issue assignment is often inconsistent and error-prone, especially in large open-source projects where thousands of new issues are reported monthly. Existing automated approaches have shown promise, but many rely heavily on large volumes of project-specific training data or relational information that is often sparse and noisy, which limits their effectiveness. To address these challenges, we propose LIA (LLM-based Issue Assignment), which employs supervised fine-tuning to adapt an LLM, DeepSeek-R1-Distill-Llama-8B in this work, for automatic issue assignment. By leveraging the LLM's pretrained semantic understanding of natural language and software-related text, LIA learns to generate ranked developer recommendations directly from issue titles and descriptions. The ranking is based on the model's learned understanding of historical issue-to-developer assignments, using patterns from past tasks to infer which developers are most likely to handle new issues. Through comprehensive evaluation, we show that LIA delivers substantial improvements over both its base pretrained model and state-of-the-art baselines. It achieves up to +187.8% higher Hit@1 compared to the DeepSeek-R1-Distill-Llama-8B pretrained base model, and outperforms four leading issue assignment methods by as much as +211.2% in Hit@1 score. These results highlight the effectiveness of domain-adapted LLMs for software maintenance tasks and establish LIA as a practical, high-performing solution for issue assignment.