Domain Knowledge in Requirements Engineering: A Systematic Mapping Study

2025-06-25

AI Summary

Existing research lacks a systematic integration of how domain knowledge is effectively elicited, formalized, and sustainably maintained in Requirements Engineering (RE). To address this gap, we employed a hybrid retrieval and iterative snowballing approach to systematically analyze 75 primary studies, thereby establishing— for the first time—the foundational Knowledge-Driven Requirements Engineering (KDRE) framework. Our analysis reveals domain knowledge’s pivotal role in understanding system context, reconciling stakeholder concerns, and resolving requirement ambiguity. We further map its application patterns and challenges across mainstream requirement types (e.g., functional, non-functional), critical quality attributes (e.g., scalability, security), and knowledge management practices (e.g., traceability, evolution). The study identifies three key research directions: scalable knowledge representation, automated knowledge integration, and sustainable tool-chain embedding. This work provides RE researchers and practitioners with a rigorous methodological foundation and a clear roadmap for future investigation and practice.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Engineering
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a comprehensive overview of existing contributions, including methods, techniques, and tools to incorporate domain knowledge into RE practices. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with iterative backward and forward snowballing. [Results] In total, we found 75 papers that met our inclusion criteria. The analysis highlights the main types of requirements addressed, the most frequently considered quality attributes, and recurring challenges in the formalization, acquisition, and long-term maintenance of domain knowledge. The results provide support for researchers and practitioners in identifying established approaches and unresolved issues. The study also outlines promising directions for future research, emphasizing the development of scalable, automated, and sustainable solutions to integrate domain knowledge into RE processes. [Conclusion] The study contributes by providing a comprehensive overview that helps to build a conceptual and methodological foundation for knowledge-driven requirements engineering.

Problem

• Systematic consolidation of domain knowledge use in RE
• Methods to incorporate domain knowledge into RE practices
• Challenges in formalizing and maintaining domain knowledge

Innovation

• Systematic mapping study on domain knowledge
• Hybrid search strategy with snowballing
• Scalable automated sustainable RE solutions