An Autonomous Software Resilience: A Neuro-Symbolic Framework for Real-Time Automated Program Repair

Kokate, Aslesha

doi:https://doi.org/10.55041/ijsmt.v2i4.101

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AN AUTONOMOUS SOFTWARE RESILIENCE: A NEURO-SYMBOLIC FRAMEWORK FOR REAL-TIME AUTOMATED PROGRAM REPAIR

AUTHORS:

Aslesha Kokate

Mentor

Chaitali Kshirsagar

Affiliation

Computer Science Department Kaveri College of Arts, Science, and Commerce, Pune University

DOI: 10.55041/ijsmt.v2i4.101

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

The modern software systems have grown quickly, making manual debugging and maintenance more immoderate, ineffective, and liable to mistakes. Automated Program Repair (APR) has emerged as a promising solution; However, due to probabilistic reasoning, illusion, and insufficient formal validation, existing AI-driven techniques—particularly those based on large language models (LLMs)—often result in patches that are syntactically correct but semantically flawed. Regression errors and unreliable deployments are caused by these limitations, particularly in distributed and safety-critical settings.

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

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