Abstract

Requirements engineering remains a critical yet labor-intensive phase of software development, in which unrestricted natural-language specifications often introduce ambiguity, inconsistency, and incompleteness. Recent advances in large language models (LLMs) create new opportunities to support requirements documentation, but directly applying these models can reproduce the limitations of informal natural language. This paper presents the ITLingo-Chatbot, a knowledge-grounded conversational assistant designed to support the creation and refinement of structured requirement specifications. The system integrates LLMs with retrieval-augmented generation (RAG) using a curated knowledge base that includes grammar definitions, validation rules, specification examples, and theoretical documentation for multiple specification languages and controlled natural languages. A prototype implementation demonstrates how users can generate, refine, and validate specification fragments through an interactive workflow. Generated artifacts may subsequently be manually validated using existing ITLingo tools.

Recommended Citation

Guerra, E. & Silva, A.(2026). ITLingo-Chatbot: A Conversational Assistant for Knowledge-Grounded Requirements Engineering. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.16

Paper Type

Full Paper

DOI

10.62036/ISD.2026.16

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ITLingo-Chatbot: A Conversational Assistant for Knowledge-Grounded Requirements Engineering

Requirements engineering remains a critical yet labor-intensive phase of software development, in which unrestricted natural-language specifications often introduce ambiguity, inconsistency, and incompleteness. Recent advances in large language models (LLMs) create new opportunities to support requirements documentation, but directly applying these models can reproduce the limitations of informal natural language. This paper presents the ITLingo-Chatbot, a knowledge-grounded conversational assistant designed to support the creation and refinement of structured requirement specifications. The system integrates LLMs with retrieval-augmented generation (RAG) using a curated knowledge base that includes grammar definitions, validation rules, specification examples, and theoretical documentation for multiple specification languages and controlled natural languages. A prototype implementation demonstrates how users can generate, refine, and validate specification fragments through an interactive workflow. Generated artifacts may subsequently be manually validated using existing ITLingo tools.