AI-Augmented Quality Engineering for Performance Optimization and Test Orchestration in Distributed Systems
Abstract
As the level of distributed systems increase in complexity, so does the need to devise better quality engineering techniques to ensure performance and reliability. The ongoing research study explores an AI-supported quality engineering system that can be deployed to optimize the operation and test-runs in the distributed systems. The traditional quality assurance methods cannot typically match the dynamic and scalability requirements of a distributed system. To meet this, the paper at hand proposes a new framework, a hybridic approach between the practice of artificial intelligence (AI) and quality engineering that provide an automatic way to optimize performance and programmatically coordinate the testing.
The framework consists of three core components: (1) AI-based monitoring of performance, (2) intelligent orchestration of tests and (3) feedback loops to dynamic optimization. Machine learning algorithms and other AI models can predict how a system will behave in various conditions and can identify performance bottlenecks on an on-demand basis. The AI methods of scheduling and assigning resources used to schedule the tests are automated and can adapt to the system workload as it varies. Testing with the introduction of AI is quicker and more accurate, which increases the dependability of systems, as well as reducing time spent on the testing process.
The other benefits of AI-enhanced quality engineering, such as a reduction in human communication, an increase in test coverage, and more effective use of resources are also described in this paper. Real-world case studies have demonstrated the practical applicability of the framework to large-scale distributed systems, by providing evidence that the framework can significantly enhance system performance as well as simplify the testing process
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
12835-12846 |
Published |
October 10, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr.R.Sugumar (%2024). AI-Augmented Quality Engineering for Performance Optimization and Test Orchestration in Distributed Systems. International Journal of Science, Research and Technology , Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12835-12846. https://doi.org/10.15662/IJSRAT.2024.0705008 |
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