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In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software’s effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation.
García et al. (Mon,) studied this question.