Automated Requirements Relation Extraction

• URL: http://arxiv.org/abs/2401.12075v2
• Date: Thu, 20 Mar 2025 15:30:25 GMT
• Title: Automated Requirements Relation Extraction
• Authors: Quim Motger, Xavier Franch,
• Abstract summary: This chapter aims at providing a clear perspective on the theoretical and practical fundamentals in the field of natural language-based relation extraction.<n>We first describe the fundamentals of requirements relations based on the most relevant literature in the field, including the most common requirements relations types.<n>The core of the chapter is composed by two main sections: (i) natural language techniques for the identification and categorization of equirements relations (i.e., syntactic vs. semantic techniques) and (ii) information extraction methods for the task of relation extraction.
• Score: 4.110571395660999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the context of requirements engineering, relation extraction involves identifying and documenting the associations between different requirements artefacts. When dealing with textual requirements (i.e., requirements expressed using natural language), relation extraction becomes a cognitively challenging task, especially in terms of ambiguity and required effort from domain-experts. Hence, in highly-adaptive, large-scale environments, effective and efficient automated relation extraction using natural language processing techniques becomes essential. In this chapter, we present a comprehensive overview of natural language-based relation extraction from text-based requirements. We initially describe the fundamentals of requirements relations based on the most relevant literature in the field, including the most common requirements relations types. The core of the chapter is composed by two main sections: (i) natural language techniques for the identification and categorization of equirements relations (i.e., syntactic vs. semantic techniques), and (ii) information extraction methods for the task of relation extraction (i.e., retrieval-based vs. machine learning-based methods). We complement this analysis with the state-of-the-art challenges and the envisioned future research directions. Overall, this chapter aims at providing a clear perspective on the theoretical and practical fundamentals in the field of natural language-based relation extraction.

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