02285nam0 22005533i 450 VAN027760120240719024520.668N978303097560920240610d2022 |0itac50 baengCH|||| |||||In the Tradition of Thurston IIGeometry and GroupsKen’ichi Ohshika, Athanase Papadopoulos editorsChamSpringer2022x, 526 p.ill.24 cm20-XXGroup theory and generalizations [MSC 2020]VANC019715MF51-XXGeometry [MSC 2020]VANC019810MFAutomatic groupsKW:KCombination theorems holomorphic dynamicsKW:KCombination theorems in Kleinian groupsKW:KCombinatorics of right-angled Artin groupsKW:KComplex hyperbolic Kleinian groupsKW:KCone 3-manifoldsKW:KGroup actionsKW:KHolomorphic dynamicsKW:KHyperbolic endsKW:KHyperbolic geometryKW:KInterval dynamicsKW:KIteration of rational mapsKW:KMapping class groupsKW:KMöbius structuresKW:KSurface Group RepresentationsKW:KSurgeries in representation varietiesKW:KThurston's normKW:KTopology of 3-manifoldsKW:KTriangulationsKW:Kcombination theorems in hyperbolic groupsKW:KCHChamVANL001889OhshikaKen’ichiVANV203971PapadopoulosAthanaseVANV043608Springer <editore>VANV108073650ITSOL20240726RICAhttps://doi.org/10.1007/978-3-030-97560-9E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN0277601BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08CONS e-Book 8755 08eMF8755 20240618 In the tradition of Thurston II2999421UNICAMPANIA04668nam 22005415 450 991103495460332120251022130423.0979-88-6881-829-510.1007/979-8-8688-1829-5(CKB)41696154400041(MiAaPQ)EBC32372240(Au-PeEL)EBL32372240(CaSebORM)9798868818295(OCoLC)1546968527(OCoLC-P)1546968527(DE-He213)979-8-8688-1829-5(EXLCZ)994169615440004120251022d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAI-Driven Software Testing Transforming Software Testing with Artificial Intelligence and Machine Learning /by Srinivasa Rao Bittla1st ed. 2025.Berkeley, CA :Apress :Imprint: Apress,2025.1 online resource (391 pages)Professional and Applied Computing SeriesDescription based upon print version of record.979-88-6881-828-8 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.AI-Driven Software Testing explores how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing quality engineering (QE), making testing more intelligent, efficient, and adaptive. The book begins by examining the critical role of QE in modern software development and the paradigm shift introduced by AI/ML. It traces the evolution of software testing, from manual approaches to AI-powered automation, highlighting key innovations that enhance accuracy, speed, and scalability. Readers will gain a deep understanding of quality engineering in the age of AI, comparing traditional and AI-driven testing methodologies to uncover their advantages and challenges. Moving into practical applications, the book delves into AI-enhanced test planning, execution, and defect management. It explores AI-driven test case development, intelligent test environments, and real-time monitoring techniques that streamline the testing lifecycle. Additionally, it covers AI’s impact on continuous integration and delivery (CI/CD), predictive analytics for failure prevention, and strategies for scaling AI-driven testing across cloud platforms. Finally, it looks ahead to the future of AI in software testing, discussing emerging trends, ethical considerations, and the evolving role of QE professionals in an AI-first world. With real-world case studies and actionable insights, AI-Driven Software Testing is an essential guide for QE engineers, developers, and tech leaders looking to harness AI for smarter, faster, and more reliable software testing. What you will learn: • What are the key principles of AI/ML-driven quality engineering • What is intelligent test case generation and adaptive test automation • Explore predictive analytics for defect prevention and risk assessment • Understand integration of AI/ML tools in CI/CD pipelines.Professional and Applied Computing SeriesComputer softwareTestingArtificial intelligenceMachine learningComputer softwareTesting.Artificial intelligence.Machine learning.005.1/4Bittla Srinivasa Rao1852974MiAaPQMiAaPQMiAaPQBOOK9911034954603321AI-Driven Software Testing4448976UNINA