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.
Abstract (translated)
大型语言模型的引入引发了对软件开发模式的重大革新和重新思考。随后，软件工程研究领域产生了大量的工具和方法。在本文中，我们加入这场变革浪潮，提出了解决两项任务的代理式AI解决方案。首先，我们开发了一种从详细需求描述自动生成测试场景的方法。这种方法依赖于多个专门的工作代理构成星形拓扑结构，并由位于中心的监督代理进行协调。我们在一个实际案例中展示了该方法的能力。其次，我们为软件工程文档中的文件检索任务开发了一个代理式AI解决方案。我们的方案支持在与单一软件开发相关的大量文档集合上执行各种用例，包括搜索、问答、追踪变更和长文档摘要生成等。在这种情况下，每个用例都有一个特定的基于大语言模型（LLM）的代理来处理所有相关子任务。最后，我们展望了未来的研究方向。
URL
https://arxiv.org/abs/2602.04726
