Handbook on Natural Language Processing for Requirements Engineering
This handbook provides a comprehensive guide on how natural language processing (NLP) can be leveraged to enhance various aspects of requirements engineering (RE), leading the reader from the exploration of fundamental concepts and techniques to the practical implementation of NLP for RE solutions i...
I tiakina i:
| Kaituhi rangatōpū: | |
|---|---|
| Ētahi atu kaituhi: | , |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Putanga: | 1st ed. 2025. |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1007/978-3-031-73143-3 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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Rārangi ihirangi:
- 1. Handbook on Natural Language Processing for Requirements Engineering: Overview
- Part I: NLP for Downstream RE Tasks
- 2. Machine Learning for Requirements Classification
- 3. Requirements Similarity and Retrieval
- 4. Natural Language Processing for Requirements Traceability
- 5. Detecting Defects in Natural Language Requirements Specifications
- 6. Automated Requirements Terminology Extraction
- 7. Automated Requirements Relations Extraction
- Part II: NLP for Specialised Types of Requirements and Artefacts
- 8. Legal Requirements Analysis: A Regulatory Compliance Perspective
- 9. Privacy Requirements Acquisition and Analysis
- 10. On the Automated Processing of User Feedback
- 11. Mining Issue Trackers: Concepts and Techniques
- 12. Automated Analysis of User Story Requirements
- Part III: NLP for RE in Practice
- 13. NLP4RE Tools: Classification, Overview, and Management
- 14. Empirical Evaluation of Tools for Hairy Natural Language Requirements Engineering Tasks
- 15. Practical Guidelines for the Selection and Evaluation of Natural Language Processing Techniques in Requirements Engineering
- 16. Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline
- 17. Dealing with Data for RE: Mitigating Challenges while Using NLP and Generative AI.