AI-Powered Automated Testing with Machine learning Algorithms: Enhancing Efficiency and Accuracy in Quality Assurance

Michael Roberts

Abstract


In recent years, Artificial Intelligence (AI) has emerged as a transformative force across various industries, including Quality Assurance (QA). This article explores the potential of AI-powered automated testing tools in enhancing the efficiency and accuracy of software testing processes. It examines the benefits of integrating AI into automated testing, such as reducing human error, increasing test coverage, and accelerating test execution. By presenting 15 detailed tables that compare traditional and AI-driven testing approaches, highlight key metrics, and evaluate case studies, this article aims to provide a comprehensive understanding of how AI technologies are reshaping the QA landscape. The conclusion synthesizes insights and discusses the future trajectory of AI in QA.

Full Text:

PDF

References


Altun, A., & Yildirim, M. (2022). A research on the new generation artificial intelligence: GPT-3 model. IEEE Access, 10, 12345–12356. https://doi.org/10.1109/ACCESS.2022.9998298

Munagandla, V. B., Vadde, B. C., & Dandyala, S. S. V. (2020). Cloud-Driven Data Integration for Enhanced Learning Analytics in Higher Education LMS. Revista de Inteligencia Artificial en Medicina, 11(1), 279-299.

Nersu, S. R. K., Kathram, S. R., & Mandaloju, N. (2020). Cybersecurity Challenges in Data Integration: A Case Study of ETL Pipelines. Revista de Inteligencia Artificial en Medicina, 11(1), 422-439.

Kathram, S. R., & Nersu, S. R. K. (2020). Adopting CICD Pipelines in Project Management Bridging the Gap Between Development and Operations. Revista de Inteligencia Artificial en Medicina, 11(1), 440- 461.

Vadde, B. C., Munagandla, V. B., & Dandyala, S. S. V. (2021). Enhancing Research Collaboration in Higher Education with Cloud Data Integration. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 366385.

Kathram, S. R., & Nersu, S. R. K. (2022). Effective Resource Allocation in Distributed Teams: Addressing the Challenges of Remote Project Management. Revista de Inteligencia Artificial en Medicina, 13(1), 615-634.

Nersu, S. R. K., & Kathram, S. R. (2022). Harnessing Federated Learning for Secure Distributed ETL Pipelines. Revista de Inteligencia Artificial en Medicina, 13(1), 592-615.

Mandaloju, N., kumar Karne, V., Srinivas, N., & Nadimpalli, S. V. (2021). Overcoming Challenges in Salesforce Lightning Testing with AI Solutions. ESP Journal of Engineering & Technology Advancements (ESP-JETA), 1(1), 228-238.

Kothamali, P. R., & Banik, S. (2019). Leveraging Machine Learning Algorithms in QA for Predictive Defect Tracking and Risk Management. International Journal of Advanced Engineering Technologies and Innovations, 1(4), 103-120.

Banik, S., & Kothamali, P. R. (2019). Developing an End-to-End QA Strategy for Secure Software: Insights from SQA Management. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 10(1), 125-155.

Kothamali, P. R., & Banik, S. (2019). Building Secure Software Systems: A Case Study on Integrating QA with Ethical Hacking Practices. Revista de Inteligencia Artificial en Medicina, 10(1), 163-191.

Kothamali, P. R., & Banik, S. (2019). The Role of Quality Assurance in Safeguarding Healthcare Software: A Cybersecurity Perspective. Revista de Inteligencia Artificial en Medicina, 10(1), 192-228.

Kothamali, P. R., & Banik, S. (2020). The Future of Threat Detection with ML. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 133-152.

Banik, S., Dandyala, S. S. M., & Nadimpalli, S. V. (2020). Introduction to Machine Learning in Cybersecurity. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 180-204.

Kothamali, P. R., Banik, S., & Nadimpalli, S. V. (2020). Introduction to Threat Detection in Cybersecurity. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 113- 132.

Kothamali, P. R., Banik, S., & Nadimpalli, S. V. (2021). Feature Engineering for Effective Threat Detection. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 341-358.

Banik, S., & Dandyala, S. S. M. (2021). Unsupervised Learning Techniques in Cybersecurity. Revista de Inteligencia Artificial en Medicina, 12(1), 384-406.

Kothamali, P. R., & Banik, S. (2021). Data Sources for Machine Learning Models in Cybersecurity. Revista de Inteligencia Artificial en Medicina, 12(1), 358-383.

Kothamali, P. R., Banik, S., & Nadimpalli, S. V. (2020). Challenges in Applying ML to Cybersecurity. Revista de Inteligencia Artificial en Medicina, 11(1), 214-256.

Kothamali, P. R., & Banik, S. (2022). Limitations of Signature-Based Threat Detection. Revista de Inteligencia Artificial en Medicina, 13(1), 381-391.

Kothamali, P. R., & Banik, S. (2020). The Future of Threat Detection with ML. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 133-152.

Kothamali, P. R., Banik, S., & Nadimpalli, S. V. (2021). Feature Engineering for Effective Threat Detection. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 341-358.

Kothamali, P. R., & Banik, S. (2021). Data Sources for Machine Learning Models in Cybersecurity. Revista de Inteligencia Artificial en Medicina, 12(1), 358-383.

Kothamali, P. R., & Banik, S. (2022). Limitations of Signature-Based Threat Detection. Revista de Inteligencia Artificial en Medicina, 13(1), 381-391.

Kothamali, P. R., Mandaloju, N., & Dandyala, S. S. M. (2022). Optimizing Resource Management in Smart Cities with AI. Unique Endeavor in Business & Social Sciences, 1(1), 174-191. https://unbss.com/index.php/unbss/article/view/54


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.