COGNITIVE AUTOMATION FRAMEWORK FOR SELF- EVOLVING SOFTWARE SYSTEMS AND AUTONOMOUS DEBUGGING

The issue of software maintenance is on the rise due to the increasing complexity of the systems and the volatile nature of the environment which requires the system to be debugged, updated, and optimized continuously. The conventional methods are not scalable, and a mean time to fix (MTTR) of critical bugs frequently rises to over 48 hours, and manual patches nearly a quarter to half the development time. Use cognitive automation models of self-evolving software systems and autonomous debugging- the latter use AI to build intelligent, adaptive architectures, learn by running, self-codebase and software which increase and solve problems automatically. Such frameworks consist fundamentally of knowledge graphs to perform semantic understanding of codebases, large language models (LLMs) to perform reasoning on anomalies, and reinforcement learning agents to perform continuous self-improvement. Multi-agent systems are observed, compiled, and debugged, and are examined through data-oriented designs, synthesize fixes through program generation, and checked through simulated rollback testing, simulating human cognition until machine speeds. Its results show revolutionary benefits: 65-75% freedom in evolution cycles, decreasing MTTR by 70% (hours to minutes); 80% accuracy in zero-touch debugging in 500+ cases; 40% faster decision loops; and 85% maintenance rates in production when compared with other tools, such as static analyzers. These results hold a promise of strong ecosystems where the software is not just surviving but also living well on its own, cutting down on expenses and providing the real business smarts.