Supporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generation

• URL: http://arxiv.org/abs/2602.04726v1
• Date: Wed, 04 Feb 2026 16:33:16 GMT
• Title: Supporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generation
• Authors: Marian Kica, Lukas Radosky, David Slivka, Karin Kubinova, Daniel Dovhun, Tomas Uhercik, Erik Bircak, Ivan Polasek,
• Abstract summary: We introduce agentic AI solutions for two software engineering tasks.<n>First, we developed a solution for automatic test scenario generation from a detailed requirements description.<n>Second, we developed an agentic AI solution for the document retrieval task in the context of software engineering documents.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The introduction of large language models ignited great retooling and rethinking of the software development models. The ensuing response of software engineering research yielded a massive body of tools and approaches. In this paper, we join the hassle by introducing agentic AI solutions for two tasks. First, we developed a solution for automatic test scenario generation from a detailed requirements description. This approach relies on specialized worker agents forming a star topology with the supervisor agent in the middle. We demonstrate its capabilities on a real-world example. Second, we developed an agentic AI solution for the document retrieval task in the context of software engineering documents. Our solution enables performing various use cases on a body of documents related to the development of a single software, including search, question answering, tracking changes, and large document summarization. In this case, each use case is handled by a dedicated LLM-based agent, which performs all subtasks related to the corresponding use case. We conclude by hinting at the future perspectives of our line of research.

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