Paper Title : AI-driven Performance Testing Framework for Mobile Applications

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International Journal of Software Engineering & Applications(IJSEA)

ISSN:0975-3834 [Online]; 0975-4679 [Print]

ERA Indexed, H Index 34

Web Page URL : https://airccse.org/journal/ijsea/ijsea.html

Current Issue link: https://www.airccse.org/journal/ijsea/vol15.html

Paper Title : AI-driven Performance Testing Framework for Mobile Applications

Authors

Vinaysimha Varma Yadavali, USA

Abstract

The rapid proliferation of mobile applications across diverse platforms has introduced unprecedented challenges in ensuring optimal performance under varying conditions. Traditional performance testing techniques often struggle to address the complexity of mobile environments, characterized by diverse devices, dynamic network conditions, and resource constraints. This paper presents an AI-Driven Performance Testing Framework for Mobile Applications, designed to revolutionize the way performance bottlenecks are identified and addressed.

The proposed framework leverages artificial intelligence to automate the testing process, dynamically adapt to real-world scenarios, and provide actionable insights for developers. Key innovations include AI-powered workload generation that mimics realistic user behaviors, anomaly detection to uncover hidden performance issues, and predictive analytics to anticipate future bottlenecks. The framework integrates seamlessly with CI/CD pipelines, ensuring continuous and scalable performance assurance.

To validate its effectiveness, we conducted extensive evaluations across multiple mobile applications, demonstrating significant improvements in test accuracy, efficiency, and resource utilization. By addressing critical challenges such as device diversity, latency variability, and resource optimization, this research establishes a foundation for the next generation of performance testing tools tailored to the unique demands of mobile applications.

Keywords

AI-Driven Testing, Mobile Application Performance, Performance Bottlenecks, Predictive Analytics, Workload Simulation, Anomaly Detection, Resource Optimization, CI/CD Integration, User Behavior Modeling, Adaptive Performance Frameworks.

Abstract URL :https://aircconline.com/abstract/ijsea/v15n6/15624ijsea03.html

Article URL :https://aircconline.com/ijsea/V15N6/15624ijsea03.pdf

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