CohortSync: Scalable Micro-Cohort-Based Protocol for Consensus and Reconciliation in Distributed Systems

Abstract

Software debugging remains one of the most time-consuming and complex tasks in software development. Traditional
debugging methods require manual analysis, which is labor-intensive, error-prone, and inefficient, especially for
large-scale systems. The rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) have enabled
automation in software debugging through self-learning mechanisms.
This paper presents a novel AI-augmented debugging framework that leverages self-learning techniques to detect,
diagnose, and autonomously fix software bugs. The approach integrates Natural Language Processing (NLP) for bug
report analysis, Deep Learning (DL) models for pattern recognition, Large Language Models (LLMs) for intelligent
code suggestions, and Reinforcement Learning (RL) for iterative improvement.
Experimental evaluations demonstrate that the proposed AI-driven debugging system significantly enhances bug
detection accuracy, reduces the time required for bug resolution, and improves software reliability. We also discuss
the challenges associated with AI-based debugging, including the quality of training datasets, ethical concerns, model
interpretability, and computational requirements. The study concludes with insights into the future of self-learning
debugging systems in the software engineering landscape.