AI-Driven Test Automation Frameworks for the Modern Software Quality Engineering
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P127Keywords:
AI-Driven Test Automation, Cognitive Software Testing, Self-Evolving Test Frameworks, Predictive Defect Detection, Cross-Domain Testing IntelligenceAbstract
The emergence of Artificial Intelligence (AI) technology in software testing and quality engineering has played a significant role in intelligent automation and analysis. Current test automation models may face limitations in terms of flexibility and ability to predict defects and allocate test resources effectively. This paper proposes a novel adaptive test-based test automation model that leverages the capabilities of AI technology to improve the quality engineering of software systems. The proposed framework is a combination of predictive analysis and test execution process to avoid redundancy of test cases and maximize the effectiveness of test cases. Moreover, it also contains learning algorithms to maximize the accuracy of test cases through the analysis of past test results and software behavior models. The experimental evaluation confirms that the proposed framework can provide better test coverage, lower test execution time, and enhanced software reliability compared to existing software automation techniques. Additionally, the framework is highly scalable and adaptable, enabling its incorporation into agile and DevOps development methodologies to ensure ongoing Quality Assurance and intelligent testing. The findings prove that AI-assisted automation models have tremendous potential.
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