# Natural Language Processing for Requirements Traceability

arXiv: https://arxiv.org/abs/2405.10845 · HTML: https://mlsanity.com/paper/2405.10845/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2024.

## Built on by (most cited first, 5 of 5)

- [Past, Present, and Future of Bug Tracking in the Generative AI Era](https://mlsanity.com/paper/2510.08005/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development](https://mlsanity.com/paper/2607.18886/) (2026)
- [Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development](https://mlsanity.com/paper/2510.04380/) (2025)
- [Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph](https://mlsanity.com/paper/2604.18883/) (2026)

## Abstract (arXiv)

Traceability, the ability to trace relevant software artifacts to support reasoning about the quality of the software and its development process, plays a crucial role in requirements and software engineering, particularly for safety-critical systems. In this chapter, we provide a comprehensive overview of the representative tasks in requirement traceability for which natural language processing (NLP) and related techniques have made considerable progress in the past decade. We first present the definition of traceability in the context of requirements and the overall engineering process, as well as other important concepts related to traceability tasks. Then, we discuss two tasks in detail, including trace link recovery and trace link maintenance. We also introduce two other related tasks concerning when trace links are used in practical contexts. For each task, we explain the characteristics of the task, how it can be approached through NLP techniques, and how to design and conduct the experiment to demonstrate the performance of the NLP techniques. We further discuss practical considerations on how to effectively apply NLP techniques and assess their effectiveness regarding the data set collection, the metrics selection, and the role of humans when evaluating the NLP approaches. Overall, this chapter prepares the readers with the fundamental knowledge of designing automated traceability solutions enabled by NLP in practice.