Volume 15, Number 6
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.