Impact of AI in Automation Testing and Quality Assurance
Kapade, Kalpesh (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024091225064
https://urn.fi/URN:NBN:fi:amk-2024091225064
Tiivistelmä
The rapid advancement of artificial intelligence (AI) has revolutionized various industries, including software testing and quality assurance (QA). This study investigated the "Impact of AI in Automation Testing and Quality Assurance" to address the pressing need for efficient and effective testing methodologies in the era of complex software systems.
To tackle this problem, the study explored the integration of AI techniques, including machine learning and natural language processing, into automation testing and QA processes. By leveraging AI, the study aimed to automate test case generation, optimize test coverage, and enhance defect detection capabilities, thereby improving the effectiveness of QA efforts.
Key results and findings of the study included significant improvements in testing efficiency and QA effectiveness, with AI-driven automation reducing test case creation time, increasing test coverage, and enhancing defect detection rates
Overall, this study highlights the transformative potential of AI in automation testing and QA, offering insights into its benefits, challenges, and future directions for research and implementation in software testing practices.
To tackle this problem, the study explored the integration of AI techniques, including machine learning and natural language processing, into automation testing and QA processes. By leveraging AI, the study aimed to automate test case generation, optimize test coverage, and enhance defect detection capabilities, thereby improving the effectiveness of QA efforts.
Key results and findings of the study included significant improvements in testing efficiency and QA effectiveness, with AI-driven automation reducing test case creation time, increasing test coverage, and enhancing defect detection rates
Overall, this study highlights the transformative potential of AI in automation testing and QA, offering insights into its benefits, challenges, and future directions for research and implementation in software testing practices.