Self-Evolving Programs: A Novel Approach Leveraging LLMs and Quine Programs

In an era where software systems undergo rapid evolution, the demand for self-evolving programs has received much attention. This paper introduces a groundbreaking methodology that amalgamates the predictive power of Large Language Model-Based Methods with the self-replicating nature of Quine Programs to organize self-evolving software. This innovative integration not only facilitates dynamic code adaptation to fluctuating runtime conditions but also pioneers a shift from traditional static coding paradigms. Through this approach, we aim to revolutionize software development, particularly enhancing security and performance with a focus on countering selfish mining attacks in Bitcoin. It should be noted that the suggested approach has not yet been considered in the field of software engineering, and its applications are not limited to software security. It has high potential to be used in every domain that requires adaptive programs. Since the suggested approach is novel, the proposed solution in Bitcoin is also novel. The results of simulation show the efficiency of the proposed solution.