Reimagining Self-Adaptation in the Age of Large Language Models
<|reference_start|>Reimagining Self-Adaptation in the Age of Large Language Models: Modern software systems are subjected to various types of uncertainties arising from context, environment, etc. To this end, self-adaptation techniques have been sought out as potential solutions. Although recent advances in self-adaptation through the use of ML techniques have demonstrated promising results, the capabilities are limited by constraints imposed by the ML techniques, such as the need for training samples, the ability to generalize, etc. Recent advancements in Generative AI (GenAI) open up new possibilities as it is trained on massive amounts of data, potentially enabling the interpretation of uncertainties and synthesis of adaptation strategies. In this context, this paper presents a vision for using GenAI, particularly Large Language Models (LLMs), to enhance the effectiveness and efficiency of architectural adaptation. Drawing parallels with human operators, we propose that LLMs can autonomously generate similar, context-sensitive adaptation strategies through its advanced natural language processing capabilities. This method allows software systems to understand their operational state and implement adaptations that align with their architectural requirements and environmental changes. By integrating LLMs into the self-adaptive system architecture, we facilitate nuanced decision-making that mirrors human-like adaptive reasoning. A case study with the SWIM exemplar system provides promising results, indicating that LLMs can potentially handle different adaptation scenarios. Our findings suggest that GenAI has significant potential to improve software systems' dynamic adaptability and resilience.<|reference_end|>
@article{donakanti2024reimagining,
    title={Reimagining Self-Adaptation in the Age of Large Language Models},
    author={Raghav Donakanti, Prakhar Jain, Shubham Kulkarni, Karthik
  Vaidhyanathan},
    journal={arXiv preprint arXiv:2404.09866},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09866},
    primaryClass={cs.SE}
}