Modernizing Testing: A Comparative Review of Test Automation Frameworks and AI Tools.
Saved in:
| Title: | Modernizing Testing: A Comparative Review of Test Automation Frameworks and AI Tools. |
|---|---|
| Authors: | Raju, Sothy Sundara, Leong, Wai Yie |
| Source: | INTI Journal; 2025, Vol. 2025 Issue 5, p1-9, 9p |
| Subject Terms: | COMPUTER software testing, ARTIFICIAL intelligence, QUALITY assurance, COMPUTER software quality control, MIXED methods research, COMPARATIVE studies |
| Abstract: | Artificial Intelligence has emerged as a revolution in software testing due to the software industry's rapid expansion, allowing Quality Assurance (QA) teams to produce higher-quality software more quickly and effectively. The comparative assessment of test automation frameworks and Artificial Intelligence (AI) powered tools presented in this journal emphasises the revolutionary potential of incorporating advanced AI capabilities into software testing procedures. The objective of this study is to create a framework that will enable organisations to implement AI-driven automation in software testing that is compatible with their requirements. The expected results from this research are to come up with a framework that improves accuracy, scalability, and adherence to software standards while minimizing manual effort and increasing overall testing efficiency. The methodology combines questionnaires and a literature review to discover the organisation's automation technologies and their influence on increasing product quality. A hybrid methodology will be used for this study that will have both quantitative and qualitative data via surveys and interviews review to discover the organisation's automation technologies and their influence on increasing product quality. [ABSTRACT FROM AUTHOR] |
| Copyright of INTI Journal is the property of INTI International University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Nájsť tento článok vo Web of Science Be the first to leave a comment!