COGNITIVE AUTOMATION FRAMEWORK FOR SELFEVOLVING 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 criti...
| Publicado en: | Scientific Culture Vol. 12; no. 2, Part 1; pp. 1151 - 1157 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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University of the Aegean
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=191995807&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 191995807 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 2, Part 1 pid: 47715 pub: University of the Aegean artinfo: ui: 191995807 10.5281/zenodo.122.12698 ppf: 1151 ppct: 6 formats: tig: atl: COGNITIVE AUTOMATION FRAMEWORK FOR SELFEVOLVING SOFTWARE SYSTEMS AND AUTONOMOUS DEBUGGING. aug: au: Bansal, Rishab Tiwari, Sujeet Kumar Sharma, Richa Dasari, Hari Prasad Kesarpu, Sagar Ranjankar, Pavankumar Balaji affil: Independent Researcher, Fremont, USA. Independent Researcher & IEEE Member, USA. Senior Consultant, CGI Tech and Solutions Inc, Raleigh, NC, USA. Expert Infrastructure Engineer, Leading Financial Tech Company, Aldie, VA, USA. Expert Application Engineer, Leading Financial Tech Company, Herndon, Virginia, USA. Data Support Specialist, Jefferson County Housing Authority, Colorado, USA. su: Software maintenance Automation software Self-adaptive software Reinforcement learning Fault diagnosis Knowledge graphs Multiagent systems Language models sug: subj: Software maintenance Automation software Self-adaptive software Reinforcement learning Fault diagnosis Knowledge graphs Multiagent systems Language models keyword: Anomaly Detection Autonomous Debugging Cognitive Automation Knowledge Graphs Large Language Models Multi-Agent Architecture Program Synthesis Reinforcement Learning Self-Evolving Systems Zero-Touch Fixes ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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