Gardado en:
| Autor Principal: | |
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| Formato: | Recurso digital |
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Zenodo
2026
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| Subjects: | |
| Acceso en liña: | https://doi.org/10.5281/zenodo.18338045 |
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Table of Contents:
- Manual software testing remains a critical phase in the software development lifecycle; however, it is often time-consuming, error-prone, and heavily dependent on human effort for bug identification, documentation, and test case management. With the increasing complexity of modern applications, traditional bug tracking tools lack intelligent assistance for visual defect detection and structured test management. This paper presents the design and development of an AI-assisted visual test and bug management platform aimed at improving the efficiency and accuracy of manual testers. The proposed system integrates visual-based bug reporting, AI-driven bug description generation, reusable test case management, and analytical dashboards within a unified platform. The solution leverages artificial intelligence models for image-based issue understanding and natural language processing to generate structured bug reports automatically. A modular architecture is implemented using modern web technologies to ensure scalability, usability, and ease of integration. Experimental evaluation demonstrates that the platform significantly reduces bug reporting time, improves defect documentation quality, and enhances tester productivity. The system provides a practical and cost-effective solution for teams transitioning from traditional manual testing processes to AI-assisted quality assurance workflows.