中文标题#
採用大型語言模型進行自動化系統整合
英文标题#
Adopting Large Language Models to Automated System Integration
中文摘要#
現代企業計算系統整合多個子系統，通過產生湧現行為來解決共同任務。 一種廣泛的方法是使用基於 Web 技術（如 REST 或 OpenAPI）實現的服務，它們分別提供互動機制和服務文檔標準。 每個服務代表特定的業務功能，允許封裝和更易於維護。 儘管在單個服務層面減少了維護成本，但整合複雜性卻增加了。 因此，自動服務組合方法應運而生，以緩解這一問題。 然而，由於這些方法依賴於複雜的正式建模，它們在實踐中並未獲得高接受度。 在本博士論文中，我們分析了大型語言模型（LLMs）在自然語言輸入基礎上自動整合服務的應用。 結果是一個可重用的服務組合，例如程序代碼。 雖然不總是生成完全正確的結果，但結果仍然有用，因為它為整合工程師提供了接近合適解決方案的近似值，只需很少的努力即可投入運行。 我們的研究包括（i）引入一種使用 LLMs 進行自動服務組合的軟件架構，（ii）分析用於服務發現的檢索增強生成（RAG），（iii）提出一種基於自然語言查詢的服務發現新基準，以及（iv）將基準擴展到完整的服務組合場景。 我們已經將我們的軟件架構、RAG 用於服務發現的分析以及服務發現基準的提案進行了展示。 開放性問題主要是將服務發現基準擴展到服務組合場景以及改進服務組合生成，例如使用微調或 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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