Conceitos Básicos
Large Language Models (LLMs) can enhance the effectiveness and efficiency of architectural adaptation in software systems by autonomously generating context-sensitive adaptation strategies that mirror human-like adaptive reasoning.
Resumo
This paper presents a novel framework, MSE-K (Monitor, Synthesize, Execute), that integrates Large Language Models (LLMs) into the self-adaptation process for software systems. The key components of the approach are:
- Monitor: Continuously collects system logs, metrics, and other contextual data to represent the running state of the software system.
- Synthesize: The core of the approach, which leverages the capabilities of LLMs to interpret the collected data and generate appropriate adaptation decisions. This component includes a Prompt Generator, LLM Engine, and Response Parser.
- Prompt Generator: Compiles the contextual data, conversation history, and system prompts into a format that can be processed by the LLM.
- LLM Engine: Uses the provided prompt to generate an adaptation decision that aligns with the system's objectives.
- Response Parser: Formats the LLM's output into a format that can be executed by the managed system.
- Execute: Verifies the adaptation decision generated by the Synthesize component and then executes the adaptation on the running software system.
The authors demonstrate the potential of this approach through a case study using the SWIM exemplar system. The results show that the LLM-based adaptation manager can maintain stable response times and achieve high utility scores, outperforming traditional reactive adaptation managers.
The authors also discuss the future research directions, including enhancing LLMs' understanding of complex system dynamics, exploring multi-agent LLM architectures, addressing long context challenges, and integrating formal verification techniques to improve the reliability of the adaptation decisions.
Estatísticas
The average response time remained stable throughout the simulation, staying below 0.1 seconds.
The LLM-based adaptation manager achieved a utility score that is roughly 71% of the reactive adaptation manager.
Citações
"Our initial findings through evaluations on an exemplar system highlight LLMs' transformative impact on software engineering, enabling complex, human-like decision-making and strategies for software to autonomously adapt their architecture with reduced human intervention."
"This research sets a foundation for further exploration into LLMs' capabilities, striving for software that is increasingly adaptive, resilient, and efficient in an ever-evolving technological landscape."