A WEB-BASED AUTOMATED BUG DETECTION AND FIX SUGGESTION SYSTEM

Software bugs are a persistent and costly challenge in software development, with organizations spending billions annually on debugging efforts. Traditional bug detection systems are often manual, resulting in inefficiencies and delayed resolutions. This research introduces a web-based automated bug detection and fix suggestion system that addresses these limitations. The system utilizes a dynamic database, Python-based automation, and a recommender engine to detect bugs in real time and propose context-aware fixes. Key innovations include a user-friendly reporting interface, integration of historical bug data to improve recommendations, and a three-tier architecture (user, developer, and administrator) to enhance collaboration. Testing results show that the system can autonomously resolve 85% of common bugs, such as syntax errors and connectivity issues, with more complex cases escalated to experts. The framework significantly reduces debugging time by 40% compared to manual systems, providing substantial value for agile software development environments. [8] These findings demonstrate the potential for real-time automated solutions in bug detection, with implications for more efficient software maintenance and future enhancements in machine learning and AI-driven bug resolution.