# EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery

arXiv: https://arxiv.org/abs/2308.10759 · HTML: https://mlsanity.com/paper/2308.10759/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2023.

## Built on by (most cited first, 9 of 9)

- [AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion](https://mlsanity.com/paper/2601.19697/) (2026)
- [Code Search Debiasing:Improve Search Results beyond Overall Ranking Performance](https://mlsanity.com/paper/2311.14901/) (2023)
- [DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning](https://mlsanity.com/paper/2601.20615/) (2026)
- [LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery](https://mlsanity.com/paper/2508.12232/) (2025)
- [PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents](https://mlsanity.com/paper/2607.18859/) (2026)
- [PatUntrack: Automated Generating Patch Examples for Issue Reports without Tracked Insecure Code](https://mlsanity.com/paper/2408.08619/) (2024)
- [Lost in Transmission: An Information-Theoretic Account of Unsupervised Software Traceability](https://mlsanity.com/paper/2412.04704/) (2024)
- [Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity](https://mlsanity.com/paper/2509.05585/) (2025)
- [LIA: Supervised Fine-Tuning of Large Language Models for Automatic Issue Assignment](https://mlsanity.com/paper/2601.01780/) (2026)

## Abstract (arXiv)

Issue-commit links, as a type of software traceability links, play a vital role in various software development and maintenance tasks. However, they are typically deficient, as developers often forget or fail to create tags when making commits. Existing studies have deployed deep learning techniques, including pretrained models, to improve automatic issue-commit link recovery.Despite their promising performance, we argue that previous approaches have four main problems, hindering them from recovering links in large software projects. To overcome these problems, we propose an efficient and accurate pre-trained framework called EALink for issue-commit link recovery. EALink requires much fewer model parameters than existing pre-trained methods, bringing efficient training and recovery. Moreover, we design various techniques to improve the recovery accuracy of EALink. We construct a large-scale dataset and conduct extensive experiments to demonstrate the power of EALink. Results show that EALink outperforms the state-of-the-art methods by a large margin (15.23%-408.65%) on various evaluation metrics. Meanwhile, its training and inference overhead is orders of magnitude lower than existing methods.