Intellifix: AI-Powered Automated Bug Tracking and Resolution System

Software debugging remains one of the most critical and resource-intensive activities in the software development lifecycle, accounting for a substantial portion of development time and cost. Despite decades of research in fault localization and automated program repair (APR), developers continue to rely heavily on manual debugging practices due to limitations in accuracy, usability, and trust in automated systems [1], [5]. While APR techniques have demonstrated the ability to generate patches automatically, studies reveal that their effectiveness is highly dependent on the quality of generated suggestions, and incorrect or overfitting patches can negatively impact debugging outcomes [5]. Recent advancements in Artificial Intelligence (AI), particularly Large Language Models (LLMs), have significantly improved the capability of systems to understand code semantics and generate context-aware solutions. Emerging approaches, such as LLM-based repair agents, demonstrate the ability to iteratively analyze code, gather contextual information, and produce fixes in a manner similar to human developers [3]. However, challenges related to reliability, validation, and integration into real-world development workflows persist. Moreover, empirical studies indicate that developers prefer systems that augment their decision-making process rather than fully automate debugging tasks [2]. In this paper, we propose IntelliFix, an AI-driven Software-as-a-Service (SaaS) platform designed to assist developers in debugging through real-time analysis and intelligent, context-aware suggestions. The system integrates modern web technologies with LLM-based models to analyze code, detect errors, and provide actionable recommendations. Unlike traditional debugging tools, IntelliFix adopts a hybrid human-in-the-loop approach, where AI-generated insights support developers without replacing their control over the debugging process. The proposed system is evaluated across multiple programming scenarios to assess its effectiveness in improving debugging efficiency and code quality. Experimental observations indicate that IntelliFix significantly reduces debugging time, enhances error detection accuracy, and improves developer productivity. These results suggest that integrating AI-driven assistance with interactive debugging environments can bridge the gap between automated and manual debugging, paving the way for next-generation intelligent development tools.