中文标题#
采用大型语言模型进行自动化系统集成
英文标题#
Adopting Large Language Models to Automated System Integration
中文摘要#
现代企业计算系统集成多个子系统，通过产生涌现行为来解决共同任务。 一种广泛的方法是使用基于 Web 技术（如 REST 或 OpenAPI）实现的服务，它们分别提供交互机制和服务文档标准。 每个服务代表特定的业务功能，允许封装和更易于维护。 尽管在单个服务层面减少了维护成本，但集成复杂性却增加了。 因此，自动服务组合方法应运而生，以缓解这一问题。 然而，由于这些方法依赖于复杂的正式建模，它们在实践中并未获得高接受度。 在本博士论文中，我们分析了大型语言模型（LLMs）在自然语言输入基础上自动集成服务的应用。 结果是一个可重用的服务组合，例如程序代码。 虽然不总是生成完全正确的结果，但结果仍然有用，因为它为集成工程师提供了接近合适解决方案的近似值，只需很少的努力即可投入运行。 我们的研究包括（i）引入一种使用 LLMs 进行自动服务组合的软件架构，（ii）分析用于服务发现的检索增强生成（RAG），（iii）提出一种基于自然语言查询的服务发现新基准，以及（iv）将基准扩展到完整的服务组合场景。 我们已经将我们的软件架构、RAG 用于服务发现的分析以及服务发现基准的提案进行了展示。 开放性问题主要是将服务发现基准扩展到服务组合场景以及改进服务组合生成，例如使用微调或 LLM 代理。
英文摘要#
Modern enterprise computing systems integrate numerous subsystems to resolve a common task by yielding emergent behavior. A widespread approach is using services implemented with Web technologies like REST or OpenAPI, which offer an interaction mechanism and service documentation standard, respectively. Each service represents a specific business functionality, allowing encapsulation and easier maintenance. Despite the reduced maintenance costs on an individual service level, increased integration complexity arises. Consequently, automated service composition approaches have arisen to mitigate this issue. Nevertheless, these approaches have not achieved high acceptance in practice due to their reliance on complex formal modeling. Within this Ph.D. thesis, we analyze the application of Large Language Models (LLMs) to automatically integrate the services based on a natural language input. The result is a reusable service composition, e.g., as program code. While not always generating entirely correct results, the result can still be helpful by providing integration engineers with a close approximation of a suitable solution, which requires little effort to become operational. Our research involves (i) introducing a software architecture for automated service composition using LLMs, (ii) analyzing Retrieval Augmented Generation (RAG) for service discovery, (iii) proposing a novel natural language query-based benchmark for service discovery, and (iv) extending the benchmark to complete service composition scenarios. We have presented our software architecture as Compositio Prompto, the analysis of RAG for service discovery, and submitted a proposal for the service discovery benchmark. Open topics are primarily the extension of the service discovery benchmark to service composition scenarios and the improvements of the service composition generation, e.g., using fine-tuning or LLM agents.
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