# Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity

arXiv: https://arxiv.org/abs/2509.05585 · HTML: https://mlsanity.com/paper/2509.05585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 89 references that are in the map.
- First posted on arXiv 6 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Traceability Transformed: Generating more Accurate Links with Pre-Trained BERT Models](https://mlsanity.com/paper/2102.04411/) (2021)
- [EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery](https://mlsanity.com/paper/2308.10759/) (2023)
- [Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks](https://mlsanity.com/paper/2005.09046/) (2020)
- [Traceability in the Wild: Automatically Augmenting Incomplete Trace Links](https://mlsanity.com/paper/1804.02433/) (2018)
- [Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data](https://mlsanity.com/paper/2107.01894/) (2021)
- [TRIAD: Automated Traceability Recovery based on Biterm-enhanced Deduction of Transitive Links among Artifacts](https://mlsanity.com/paper/2312.16854/) (2023)
- [Using Consensual Biterms from Text Structures of Requirements and Code to Improve IR-Based Traceability Recovery](https://mlsanity.com/paper/2209.01734/) (2022)
- [MPLinker: Multi-template Prompt-tuning with Adversarial Training for Issue-commit Link Recovery](https://mlsanity.com/paper/2501.19026/) (2025)
- [Traceability Support for Multi-Lingual Software Projects](https://mlsanity.com/paper/2006.16940/) (2020)
- [AVIATE: Exploiting Translation Variants of Artifacts to Improve IR-based Traceability Recovery in Bilingual Software Projects](https://mlsanity.com/paper/2409.19304/) (2024)

## Abstract (arXiv)

In the field of software traceability link recovery (TLR), textual similarity has long been regarded as the core criterion. However, in tasks involving natural language and programming language (NL-PL) artifacts, relying solely on textual similarity is limited by their semantic gap. To this end, we conducted a large-scale empirical evaluation across various types of TLR tasks, revealing the limitations of textual similarity in NL-PL scenarios. To address these limitations, we propose an approach that incorporates multiple domain-specific auxiliary strategies, identified through empirical analysis, into two models: the Heterogeneous Graph Transformer (HGT) via edge types and the prompt-based Gemini 2.5 Pro via additional input information. We then evaluated our approach using the widely studied requirements-to-code TLR task, a representative case of NL-PL TLR. Experimental results show that both the multi-strategy HGT and Gemini 2.5 Pro models outperformed their original counterparts without strategy integration. Furthermore, compared to the current state-of-the-art method HGNNLink, the multi-strategy HGT and Gemini 2.5 Pro models achieved average F1-score improvements of 3.68% and 8.84%, respectively, across twelve open-source projects, demonstrating the effectiveness of multi-strategy integration in enhancing overall model performance for the requirements-code TLR task.