Acceptance criteria (ACs) play a critical role in software development by clearly defining the conditions under which a software feature satisfies stakeholder expectations. However, manually creating accurate, comprehensive, and unambiguous acceptance criteria is challenging, particularly in user interface-intensive applications, due to the reliance on domain-specific knowledge and visual context that is not always captured by textual requirements alone. To address these challenges, we propose RAGcceptance_M2RE, a novel approach that leverages Retrieval-Augmented Generation (RAG) to generate acceptance criteria from multi-modal requirements data, including both textual documentation and visual UI information. We systematically evaluated our approach in an industrial case study involving an education-focused software system used by approximately 100,000 users. The results indicate that integrating multi-modal information significantly enhances the relevance, correctness, and comprehensibility of the generated ACs. Moreover, practitioner evaluations confirm that our approach effectively reduces manual effort, captures nuanced stakeholder intent, and provides valuable criteria that domain experts may overlook, demonstrating practical utility and significant potential for industry adoption. This research underscores the potential of multi-modal RAG techniques in streamlining software validation processes and improving development efficiency. We also make our implementation and a dataset available.

@inproceedings{paperbot9345,
  title = {Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs},
  author = {Fanyu Wang and Chetan Arora and Yonghui Liu and Kaicheng Huang and Chakkrit Tantithamthavorn and Aldeida Aleti and Dishan Sambathkumar and David Lo},
  booktitle = {ASE},
  year = {2025},
  doi = {10.1109/ase63991.2025.00275}
}