Mastering Generative AI in the Software Development Life Cycle
Language: English
Published by Independently published, 2024
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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- Title
- Mastering Generative AI in the Software Development Life Cycle
- Author
- Vemula, Anand
- Publisher
- Independently published
- Publication year
- 2024
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798327263611
**Mastering Generative AI in the Software Development Life Cycle** explores the transformative potential of generative AI in modern software development. This comprehensive guide walks readers through integrating AI technologies across each phase of the Software Development Life Cycle (SDLC), from requirements gathering and system design to implementation, testing, deployment, and maintenance.
The book begins with an overview of generative AI, explaining its core concepts, historical development, and diverse applications. It underscores the importance of the SDLC, contrasting traditional and agile methodologies, and addressing contemporary challenges in software development.
In the system design phase, readers learn how AI can automate architectural design, create rapid prototypes, and optimize resource allocation. The implementation chapter highlights AI-assisted code generation, presenting best practices for ensuring the quality and maintainability of AI-generated code. Version control and collaboration tools are also discussed to streamline the development process.
Testing is revolutionized through AI-driven automated test case generation, bug detection, and continuous integration and deployment (CI/CD). Real-world examples illustrate how AI can enhance efficiency and accuracy in these critical activities.
The deployment chapter delves into AI for deployment optimization, including predictive analysis, automated rollbacks, and resource management. It also covers AI-powered monitoring and maintenance, with techniques for anomaly detection, predictive maintenance, and automated scaling.
Maintenance and evolution are addressed with a focus on predictive maintenance using AI, adapting AI models to changing requirements, and exploring future trends like self-healing systems and advanced predictive analytics. Ethical and legal considerations, such as bias mitigation, transparency, accountability, and compliance with regulations like GDPR and HIPAA, are thoroughly examined.
Industry case studies demonstrate AI's impact on various sectors, including finance, healthcare, and e-commerce. These examples show how AI enhances fraud detection, disease prediction, personalized recommendations, and more.
The book also provides an overview of essential AI tools and technologies, offering guidance on integrating them into SDLC pipelines. It concludes with insights into emerging trends and the future of AI in software development, preparing readers for the evolving landscape of AI-driven development.
**Mastering Generative AI in the Software Development Life Cycle** is an essential resource for developers, engineers, and tech enthusiasts aiming to harness AI's power to innovate and optimize their software development processes.
The book begins with an overview of generative AI, explaining its core concepts, historical development, and diverse applications. It underscores the importance of the SDLC, contrasting traditional and agile methodologies, and addressing contemporary challenges in software development.
In the system design phase, readers learn how AI can automate architectural design, create rapid prototypes, and optimize resource allocation. The implementation chapter highlights AI-assisted code generation, presenting best practices for ensuring the quality and maintainability of AI-generated code. Version control and collaboration tools are also discussed to streamline the development process.
Testing is revolutionized through AI-driven automated test case generation, bug detection, and continuous integration and deployment (CI/CD). Real-world examples illustrate how AI can enhance efficiency and accuracy in these critical activities.
The deployment chapter delves into AI for deployment optimization, including predictive analysis, automated rollbacks, and resource management. It also covers AI-powered monitoring and maintenance, with techniques for anomaly detection, predictive maintenance, and automated scaling.
Maintenance and evolution are addressed with a focus on predictive maintenance using AI, adapting AI models to changing requirements, and exploring future trends like self-healing systems and advanced predictive analytics. Ethical and legal considerations, such as bias mitigation, transparency, accountability, and compliance with regulations like GDPR and HIPAA, are thoroughly examined.
Industry case studies demonstrate AI's impact on various sectors, including finance, healthcare, and e-commerce. These examples show how AI enhances fraud detection, disease prediction, personalized recommendations, and more.
The book also provides an overview of essential AI tools and technologies, offering guidance on integrating them into SDLC pipelines. It concludes with insights into emerging trends and the future of AI in software development, preparing readers for the evolving landscape of AI-driven development.
**Mastering Generative AI in the Software Development Life Cycle** is an essential resource for developers, engineers, and tech enthusiasts aiming to harness AI's power to innovate and optimize their software development processes.
"Synopsis" may belong to another edition of this title.
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