Citation:

Nagaraj Mandaloju, Vinod kumar Karne, Noone Srinivas, Siddhartha Varma Nadimpalli, 2021. "A Unified Approach to QA Automation in Salesforce Using AI, ML, and Cloud Computing", ESP Journal of Engineering & Technology Advancements 1(2): 244-256.

Abstract:

This study investigates the integration of Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing into Quality Assurance (QA) automation within Salesforce environments. The primary aim was to address the limitations of traditional QA methods by evaluating the impact of these advanced technologies on software quality, scalability, and operational efficiency. Employing a mixed-methods design, the study utilized simulated data to assess test coverage, defect detection rates, and resource management across three QA automation approaches: traditional, AI-enhanced, and cloud-based unified systems. The analysis revealed that AI significantly improved test coverage and defect detection, ML enhanced test generation and optimization, and cloud computing facilitated scalable and efficient testing processes. The unified approach integrating AI, ML, and cloud computing demonstrated superior performance compared to traditional methods, offering a more robust solution for managing complex Salesforce environments. These findings suggest that advanced technologies can greatly enhance QA automation, leading to improved software reliability and operational efficiency. The study underscores the importance of integrating these technologies for future QA practices.

References:

[1] Ajiga, D. I., Ndubuisi, N. L., Asuzu, O. F., Owolabi, O. R., Tubokirifuruar, T. S., & Adeleye, R. A. (2024). AI-driven predictive analytics in retail: A review of emerging trends and customer engagement strategies. International Journal of Management & Entrepreneurship Research, 6(2), 307-321.

[2] Chen, X. (2023). Efficient Algorithms for Real-Time Semantic Segmentation in Augmented Reality. Innovative Computer Sciences Journal, 9(1).

[3] Chen, X. (2023). Optimization Strategies for Reducing Energy Consumption in AI Model Training. Advances in Computer Sciences, 6(1).

[4] Chen, X. (2023). Real-Time Detection of Adversarial Attacks in Deep Learning Models. MZ Computing Journal, 4(2).

[5] Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: A customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663-680.

[6] Kaliuta, K. (2023). Personalizing the user experience in Salesforce using AI technologies. Computer-Integrated Technologies: Education, Science, Production, (52), 48-53.

[7] Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how?. International Journal of Information Management, 77, 102783.

[8] Reddy, S. R. B. (2022). Enhancing Customer Experience through AI-Powered Marketing Automation: Strategies and Best Practices for Industry 4.0. Journal of Artificial Intelligence Research, 2(1), 36-46.

[9] Tadimarri, A., Jangoan, S., Sharma, K. K., & Gurusamy, A. AI-Powered Marketing: Transforming Consumer Engagement and Brand Growth.

[10] Wang, Z., Zhu, Y., Li, Z., Wang, Z., Qin, H., & Liu, X. (2024). Graph neural network recommendation system for football formation. Applied Science and Biotechnology Journal for Advanced Research, 3(3), 33-39.

[11] VORSOBINA, M. The impact of AI-powered digital marketing operations: Empirical evidence from case studies.

Keywords:

Salesforce, Quality Assurance, Artificial Intelligence, Machine Learning, Cloud Computing