# Semantically Enhanced Software Traceability Using Deep Learning Techniques

arXiv: https://arxiv.org/abs/1804.02438 · HTML: https://mlsanity.com/paper/1804.02438/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

## Built on by (most cited first, 22 of 22)

- [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024)
- [The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review](https://mlsanity.com/paper/2206.00421/) (2022)
- [6GSoft: Software for Edge-to-Cloud Continuum](https://mlsanity.com/paper/2407.05963/) (2024)
- [Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes](https://mlsanity.com/paper/2502.09690/) (2025)
- [An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation](https://mlsanity.com/paper/2301.01224/) (2023)
- [Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding](https://mlsanity.com/paper/2504.00409/) (2025)
- [Evaluating the Use of LLMs for Documentation to Code Traceability](https://mlsanity.com/paper/2506.16440/) (2025)
- [LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery](https://mlsanity.com/paper/2508.12232/) (2025)
- [Classification of Natural Language Processing Techniques for Requirements Engineering](https://mlsanity.com/paper/2204.04282/) (2022)
- [Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models](https://mlsanity.com/paper/2607.16708/) (2026)
- [How Much Data Analytics is Enough? The ROI of Machine Learning Classification and its Application to Requirements Dependency Classification](https://mlsanity.com/paper/2109.14097/) (2021)
- [Improving Trace Link Recommendation by Using Non-Isotropic Distances and Combinations](https://mlsanity.com/paper/2307.07781/) (2023)
- [Vulnerability Detection in C/C++ Code with Deep Learning](https://mlsanity.com/paper/2405.12384/) (2024)
- [The Role of Artificial Intelligence and Machine Learning in Software Testing](https://mlsanity.com/paper/2409.02693/) (2024)
- [Lost in Transmission: An Information-Theoretic Account of Unsupervised Software Traceability](https://mlsanity.com/paper/2412.04704/) (2024)
- [Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives](https://mlsanity.com/paper/2503.13793/) (2025)
- [Working Document -- Formalising Software Requirements with Large Language Models](https://mlsanity.com/paper/2506.14627/) (2025)
- [Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development](https://mlsanity.com/paper/2510.04380/) (2025)
- [Supporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generation](https://mlsanity.com/paper/2602.04726/) (2026)
- [IncreRTL: Traceability-Guided Incremental RTL Generation under Requirement Evolution](https://mlsanity.com/paper/2603.25769/) (2026)
- [Trust-Aware Multi-Agent Traceability: Confidence-Calibrated Knowledge Graphs for Consistent Software Artifact Management](https://mlsanity.com/paper/2606.17203/) (2026)
- [Prompt Coverage Adequacy](https://mlsanity.com/paper/2607.02057/) (2026)

## Abstract (arXiv)

In most safety-critical domains the need for traceability is prescribed by certifying bodies. Trace links are generally created among requirements, design, source code, test cases and other artifacts, however, creating such links manually is time consuming and error prone. Automated solutions use information retrieval and machine learning techniques to generate trace links, however, current techniques fail to understand semantics of the software artifacts or to integrate domain knowledge into the tracing process and therefore tend to deliver imprecise and inaccurate results. In this paper, we present a solution that uses deep learning to incorporate requirements artifact semantics and domain knowledge into the tracing solution. We propose a tracing network architecture that utilizes Word Embedding and Recurrent Neural Network (RNN) models to generate trace links. Word embedding learns word vectors that represent knowledge of the domain corpus and RNN uses these word vectors to learn the sentence semantics of requirements artifacts. We trained 360 different configurations of the tracing network using existing trace links in the Positive Train Control domain and identified the Bidirectional Gated Recurrent Unit (BI-GRU) as the best model for the tracing task. BI-GRU significantly out-performed state-of-the-art tracing methods including the Vector Space Model and Latent Semantic Indexing.