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AI-driven software testing = transfo...
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Bittla, Srinivasa Rao.
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AI-driven software testing = transforming software testing with artificial intelligence and machine learning /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
AI-driven software testing/ by Srinivasa Rao Bittla.
其他題名:
transforming software testing with artificial intelligence and machine learning /
作者:
Bittla, Srinivasa Rao.
出版者:
Berkeley, CA :Apress : : 2025.,
面頁冊數:
xxxvi, 513 p. :ill., digital ;24 cm.
內容註:
Part 1 -- Chapter 1: The Role of AI and ML in Modern Software Testing -- Chapter 2: Software Testing from Manual to AI-Driven Automation -- Chapter 3: Quality Engineering in the Age of AI -- Chapter 4: Comparing Traditional and AI-Driven Testing -- Chapter 5: SDLC vs STLC Understanding the Basics -- Chapter 6: The Testing Pyramid in Traditional and AI-Driven Testing -- Part 2 -- Chapter 7: Revolutionizing Test Planning and Execution with AI/ML -- Chapter 8: Intelligent Test Case Development with AI/ML -- Chapter 9: AI/ML-Driven Test Setup and Management -- Chapter 10: AI/ML in Smart Defect Management and Resolution -- Chapter 11: Test Closure with AI/ML Reporting and Continuous Feedback -- Chapter 12: Eliminating Testing Gaps with AI/ML Precision -- Part 3 -- Chapter 13: Scaling Software Testing with AI/ML -- Chapter 14: Enhancing CI/CD Pipelines with AI/ML Driven Testing -- Chapter 15: AI/ML for Real-Time Test Execution Monitoring -- Chapter 16: Predicting Failures with AI/ML Analytics -- Chapter 17: The Future of QE with AI-Driven Testing -- Chapter 18. Next Steps to Implementing AI-Driven QE.
Contained By:
Springer Nature eBook
標題:
Computer software - Testing. -
電子資源:
https://doi.org/10.1007/979-8-8688-1829-5
ISBN:
9798868818295
AI-driven software testing = transforming software testing with artificial intelligence and machine learning /
Bittla, Srinivasa Rao.
AI-driven software testing
transforming software testing with artificial intelligence and machine learning /[electronic resource] :by Srinivasa Rao Bittla. - Berkeley, CA :Apress :2025. - xxxvi, 513 p. :ill., digital ;24 cm.
Part 1 -- Chapter 1: The Role of AI and ML in Modern Software Testing -- Chapter 2: Software Testing from Manual to AI-Driven Automation -- Chapter 3: Quality Engineering in the Age of AI -- Chapter 4: Comparing Traditional and AI-Driven Testing -- Chapter 5: SDLC vs STLC Understanding the Basics -- Chapter 6: The Testing Pyramid in Traditional and AI-Driven Testing -- Part 2 -- Chapter 7: Revolutionizing Test Planning and Execution with AI/ML -- Chapter 8: Intelligent Test Case Development with AI/ML -- Chapter 9: AI/ML-Driven Test Setup and Management -- Chapter 10: AI/ML in Smart Defect Management and Resolution -- Chapter 11: Test Closure with AI/ML Reporting and Continuous Feedback -- Chapter 12: Eliminating Testing Gaps with AI/ML Precision -- Part 3 -- Chapter 13: Scaling Software Testing with AI/ML -- Chapter 14: Enhancing CI/CD Pipelines with AI/ML Driven Testing -- Chapter 15: AI/ML for Real-Time Test Execution Monitoring -- Chapter 16: Predicting Failures with AI/ML Analytics -- Chapter 17: The Future of QE with AI-Driven Testing -- Chapter 18. Next Steps to Implementing AI-Driven QE.
ISBN: 9798868818295
Standard No.: 10.1007/979-8-8688-1829-5doiSubjects--Topical Terms:
570748
Computer software
--Testing.
LC Class. No.: QA76.76.T48 / B58 2025
Dewey Class. No.: 005.14
AI-driven software testing = transforming software testing with artificial intelligence and machine learning /
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Part 1 -- Chapter 1: The Role of AI and ML in Modern Software Testing -- Chapter 2: Software Testing from Manual to AI-Driven Automation -- Chapter 3: Quality Engineering in the Age of AI -- Chapter 4: Comparing Traditional and AI-Driven Testing -- Chapter 5: SDLC vs STLC Understanding the Basics -- Chapter 6: The Testing Pyramid in Traditional and AI-Driven Testing -- Part 2 -- Chapter 7: Revolutionizing Test Planning and Execution with AI/ML -- Chapter 8: Intelligent Test Case Development with AI/ML -- Chapter 9: AI/ML-Driven Test Setup and Management -- Chapter 10: AI/ML in Smart Defect Management and Resolution -- Chapter 11: Test Closure with AI/ML Reporting and Continuous Feedback -- Chapter 12: Eliminating Testing Gaps with AI/ML Precision -- Part 3 -- Chapter 13: Scaling Software Testing with AI/ML -- Chapter 14: Enhancing CI/CD Pipelines with AI/ML Driven Testing -- Chapter 15: AI/ML for Real-Time Test Execution Monitoring -- Chapter 16: Predicting Failures with AI/ML Analytics -- Chapter 17: The Future of QE with AI-Driven Testing -- Chapter 18. Next Steps to Implementing AI-Driven QE.
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