SKU/Artículo: AMZ-B0FJ64XJ8F
Agentic AI-Assisted Software Scalability Testing and Analysis
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.29 kg
0.29 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- This book explores the revolutionary integration of autonomous AI agents into software scalability testing and analysis. It's a comprehensive guide that bridges the gap between theoretical concepts and practical applications, making it an essential resource for anyone involved in scaling modern distributed systems. The book begins by laying the groundwork, covering the fundamentals of both scalability engineering and agentic AI. It then demonstrates how intelligent agents can transform every stage of the testing lifecycle—from test design and execution to monitoring and optimization. A key highlight is the comprehensive scalability testing result analysis framework, complete with accompanying Python code and detailed documentation available in a GitHub repository. This allows readers to immediately implement and extend the techniques discussed, providing ready-to-use tools for robust scalability analysis. The book is structured into five interconnected parts, progressively building your expertise in this emerging field: Part I: Foundations: This section defines software scalability in terms of a system's ability to handle increasing workloads while maintaining performance. It then introduces agentic AI—systems characterized by autonomy, proactivity, reactivity, and social ability—and explains why traditional scalability testing methods often fall short in today's complex environments. Part II: Test Design and Execution: Learn how AI agents can dynamically provision test environments, generate realistic synthetic test data, and simulate user behavior based on production traffic patterns. This part details how agents can autonomously execute tests with adaptive load pacing and introduce controlled failures to evaluate system resilience. Part III: Analysis and Optimization: Discover how agent-driven monitoring systems can detect anomalies, predict performance degradation, and perform root cause analysis across distributed systems. The book explains how agents can identify resource contention, recommend optimizations, and assist with capacity planning through predictive models. Part IV: Practical Framework: This part presents the practical scalability testing result analysis framework with Python code and documentation. This framework enables the application of the Universal Scalability Law and other models to analyze test results and visualize performance characteristics. Part V: Advanced Topics and Future Outlook: Explore advanced topics such as integration with CI/CD pipelines and delve into case studies across diverse architectures, including microservices, cloud-native applications, e-commerce platforms, and gaming systems. The book concludes by examining emerging technologies like Edge AI and neuromorphic computing, outlining a future of autonomous scalability engineering. Throughout the book, the author emphasizes that agentic AI complements rather than replaces human expertise. This shift transforms the role of performance engineers from manual testing to strategic oversight and complex problem-solving, making this book indispensable for performance engineers, software architects, and technology leaders navigating the challenges of scaling modern distributed systems.
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AR$73.602
49% OFF
AR$37.747
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