Automated Requirements Relations Extraction - Handbook on Natural Language Processing for Requirements Engineering

Notebook and datasets for Chapter 7 "Automated Requirements Relations Extraction" from "Handbook on Natural Language Processing for Requirements Engineering". This chapter presents a comprehensive overview of natural language-based relation extraction from text-based requirements. We structure these techniques into two different sub-sections:

1. NLP Knowledge Representation Techniques for the identification and categorization of requirements relations (i.e., syntactic vs. semantic techniques)
2. Information Extraction Methods for the task of relation extraction (i.e., retrieval-based vs. machine learning-based methods).

Citation

To cite the notebook or the book chapter, please use the following citation:

• Motger, Q., Franch, X. (2025). Automated Requirements Relations Extraction. In: Ferrari, A., Ginde, G. (eds) Handbook on Natural Language Processing for Requirements Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-73143-3_7

@Inbook{Motger2025,
    author="Motger, Quim
    and Franch, Xavier",
    editor="Ferrari, Alessio
    and Ginde, Gouri",
    title="Automated Requirements Relations Extraction",
    bookTitle="Handbook on Natural Language Processing for Requirements Engineering",
    year="2025",
    publisher="Springer Nature Switzerland",
    address="Cham",
    pages="177--206",
    isbn="978-3-031-73143-3",
    doi="10.1007/978-3-031-73143-3_7"
}

To cite the requirements data set, please use the following citation:

• Deshpande, G., Arora, C., Ruhe, G.: Data-driven elicitation and optimization of dependencies between requirements. In: 2019 IEEE 27th International Requirements Engineering Conference (RE). pp. 416–421 (2019). https://doi.org/10.1109/RE.2019.00055

To cite the ontology file, please use the following citation:

• Deshpande, G., Motger, Q., Palomares, C., Kamra, I., Biesialska, K., Franch, X., Ruhe, G., Ho, J.: Requirements dependency extraction by integrating active learning with ontology-based retrieval. In: 2020 IEEE 28th International Requirements Engineering Conference (RE). pp. 78–89 (2020). https://doi.org/10.1109/RE48521.2020.00020