AI-Assisted Requirements Engineering Using Multimodal Language Models

  • Authors

    • Karthik Raman Software Architect, Tata Consultancy Services, India. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2019PI8W8J

    Published 01-03-2019

  • Multimodal Language Models, Requirements Engineering, AI-Assisted Software Engineering, Requirements Elicitation, Requirements Classification, Requirements Validation, Traceability, Natural Language Processing, Human-AI Collaboration, Software Requirements Specification (SRS)

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    K. Raman, “AI-Assisted Requirements Engineering Using Multimodal Language Models”, ijmiet, vol. 2, no. 1, pp. 01–20, Jan. 2019, doi: 10.67228/30715628/IJMIET-2019PI8W8J.

  • Abstract

    The rapid advancement of multimodal large language models (MLLMs) has introduced new opportunities to enhance Requirements Engineering (RE) processes through automated understanding of textual, visual, and conversational artifacts. This paper investigates how MLLMs can support key RE activities—including requirements elicitation, classification, validation, conflict detection, prioritization, and documentation—by leveraging their ability to process heterogeneous project inputs such as stakeholder conversation transcripts, UI sketches, system diagrams, and domain documents. We present a conceptual framework for integrating MLLMs into existing RE workflows, highlighting their potential to reduce ambiguity, accelerate requirement refinement, and improve traceability. Through experimental evaluation and case studies, we demonstrate that MLLM-powered RE assistance can significantly increase requirement accuracy and analyst productivity. Finally, we discuss risks such as hallucination, data privacy, and model bias, and propose mitigation strategies and future research directions for reliable adoption of AI-assisted RE systems.

  • References

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