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AI in Product Testing for Enhanced Quality Assurance

by Devendra Singh Parmar, Hemlatha Kaur Saran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 63
Year of Publication: 2025
Authors: Devendra Singh Parmar, Hemlatha Kaur Saran
10.5120/ijca2025924405

Devendra Singh Parmar, Hemlatha Kaur Saran . AI in Product Testing for Enhanced Quality Assurance. International Journal of Computer Applications. 186, 63 ( Jan 2025), 14-19. DOI=10.5120/ijca2025924405

@article{ 10.5120/ijca2025924405,
author = { Devendra Singh Parmar, Hemlatha Kaur Saran },
title = { AI in Product Testing for Enhanced Quality Assurance },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2025 },
volume = { 186 },
number = { 63 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number63/ai-in-product-testing-for-enhanced-quality-assurance/ },
doi = { 10.5120/ijca2025924405 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-31T17:28:30.718932+05:30
%A Devendra Singh Parmar
%A Hemlatha Kaur Saran
%T AI in Product Testing for Enhanced Quality Assurance
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 63
%P 14-19
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Quality assurance is revamped through advancements in productivity, accuracy, and forecasting competencies by using artificial intelligence (AI) during product testing. This study intends to explore the prospect of artificial intelligence in improving quality assurance processes, including automation of test scenarios, detection of defects, and prediction of probable failures. AI-driven quality assurance employs machine learning, natural language processing, and advanced analytical techniques to make fault identification easier, speed up testing, and save costs, thus contributing to a more reliable product launch. Other discussions in the paper touch on data quality, moral dilemmas, and the requirement of a human overseer in quality assurance enhanced by AI. This research presents valuable insights regarding prospective trends and optimal methodologies in AI-driven Quality Assurance by utilizing case studies and examples pertinent to specific industries, aiming to support organizations in enhancing their product testing and overall quality.

References
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Index Terms

Computer Science
Information Sciences
The general terms that will be used throughout the paper will be Artificial Intelligence (AI)
Quality Assurance (QA)
and Quality Control (QC)

Keywords

Artificial Intelligence Quality Control Decision Making Real-time Monitoring Predictive Analytics Data-driven Insights Risk Assessment Process Optimization

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