Software Testing with Generative AI
Disponibilidad:
Fuera de stock
Fuera de stock
Peso con empaque:
0.42 kg
0.42 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Sobre este producto
- Understand generative AI systems through an engineering lens, including how probabilistic behavior alters traditional testing assumptions.
- Redefine test oracles, correctness, and validation in environments where outputs are plausible but not guaranteed to be correct.
- Use prompt engineering as a form of test design, specification, and constraint definition.
- Apply generative AI across the entire test lifecycle—from requirements analysis to regression maintenance—without eroding trust or accountability.
- Design architectures for AI-assisted testing that include retrieval grounding, validation layers, and human-in-the-loop controls.
- Detect, classify, and mitigate hallucinations and other AI-specific failure modes.
- Establish security, privacy, and trust boundaries when integrating generative AI into quality workflows.
- Operationalize generative AI in real test organizations, balancing speed, risk, and professional responsibility.
- Measure success using metrics that reflect confidence, defect detection, and risk reduction—not vanity automation numbers.
- Software test engineers, SDETs, and QA professionals working with modern systems
- Software architects and senior developers responsible for quality infrastructure
- Engineering leaders evaluating how generative AI fits into their delivery pipelines
- Researchers and graduate students studying software testing, AI-assisted engineering, or socio-technical systems
- A tool manual
- A certification or exam guide
- A collection of prompt recipes
- A claim that AI replaces testers
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