AI-Driven Test Automation for Big Data Systems in Cloud Environments

AI-based Test Automation for Cloud-Based Big Advanced Data Systems this method used AI and other new data science methods such as big data, cloud computing, etc. Current testing methodologies do not suit the environments of modern big data systems in a cloud-based environment. AI based test automation approaches: Automated test scenario preparation, examination of test events to identify defects and generation of testing issues. Big data systems, on the other hand, can periodically scan for maximum performance with an all-AI approach, carry out A/B testing, and fine-tune performance. This is particularly important for cloud environments when the architecture of the system and data processing continually changes at a high rate. Additionally, AI in test automation is capable of detecting anomalies and predicting possible system breakdown, providing early warning of issues to ensure mitigation and thereby avoiding costly downtime. This way various scenarios and data sets can be tested and covered to ensure that the best results are covered.

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Published
2025-02-21
DOI
https://doi.org/10.1109/ce2ct64011.2025.10941644
Citations
1
Primary Topic
Graph Theory and Algorithms
Type
article
Field-Weighted Citation Impact
1.28
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AI-Driven Test Automation for Big Data Systems in Cloud Environments

Gopal Kumar Gupta, Sanjeev Kumar Pippal, Ankit Kumar Gupta, Sreeprasad Govindankutty et al.
1 citations
Graph Theory and Algorithms
1.28
article

AI-Driven Test Automation for Big Data Systems in Cloud Environments

Gopal Kumar Gupta, Sanjeev Kumar Pippal, Ankit Kumar Gupta, Sreeprasad Govindankutty, Avinash Reddy Nandyala, Justin Rajakumar Maria Thason
article en
1 citations

Abstract

AI-based Test Automation for Cloud-Based Big Advanced Data Systems this method used AI and other new data science methods such as big data, cloud computing, etc. Current testing methodologies do not suit the environments of modern big data systems in a cloud-based environment. AI based test automation approaches: Automated test scenario preparation, examination of test events to identify defects and generation of testing issues. Big data systems, on the other hand, can periodically scan for maximum performance with an all-AI approach, carry out A/B testing, and fine-tune performance. This is particularly important for cloud environments when the architecture of the system and data processing continually changes at a high rate. Additionally, AI in test automation is capable of detecting anomalies and predicting possible system breakdown, providing early warning of issues to ensure mitigation and thereby avoiding costly downtime. This way various scenarios and data sets can be tested and covered to ensure that the best results are covered.

Dr. A.P.J. Abdul Kalam Technical University (IN), Texas State University (US), Rochester Institute of Technology (US), Symbiosis International University (IN), Sikkim University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 22%
Graph Theory and Algorithms
1.28
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AI-Driven Test Automation for Big Data Systems in Cloud Environments — Gopal Kumar Gupta, Sanjeev Kumar Pippal, et al. · (2025) | TGRS Research Map | TGRS