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In today's Agile and DevOps-driven software development landscape, rapid and accurate bug reporting is critical. This paper presents a next-generation automation tool powered by large language models and machine learning, innovating the bug reporting process. The tool automates various phases of bug reporting, such as failure detection, severity assessment, duplicate detection, and report generation. By addressing the limitations of manual bug reporting such as inconsistency, scalability challenges, and time inefficiencies, the proposed solution enhances the software testing workflow. Initial findings demonstrate significant time savings, reduced manual errors, and improved collaboration between testers and developers. This work establishes a foundation for fully automated bug reporting, poised to accelerate software development cycles and maintain high-quality standards.
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Patil et al. (2025) studied this question.
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