AI-Assisted Visual Test and Bug Management Platform for Manual Testers
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Veröffentlicht: |
Zenodo
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866901913576931328 |
|---|---|
| author | Mansi Rajgopal Kulkarni |
| author_facet | Mansi Rajgopal Kulkarni |
| 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. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18338045 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Assisted Visual Test and Bug Management Platform for Manual Testers Mansi Rajgopal Kulkarni Manual Testing; Bug Management; Visual Testing; Artificial Intelligence; Software Quality Assurance 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. |
| title | AI-Assisted Visual Test and Bug Management Platform for Manual Testers |
| topic | Manual Testing; Bug Management; Visual Testing; Artificial Intelligence; Software Quality Assurance |
| url | https://doi.org/10.5281/zenodo.18338045 |