Presentation at ADDO - October 10, 2024 - 330 ET
- Presentation materials including the AI infused continuum diagram
- Detailed Bibliography
- GAI Service Provider Question Sets
Generative AI has taken the world by storm, including the realm of software engineering. There’s a rush to incorporate AI tools, from code generation to test analysis. We’ll begin with a high-level overview of AI in the software development lifecycle (SDLC), then dive into the sometimes-tricky world of using Generative AI in software engineering. This is groundbreaking technology that has limitations and challenges to navigate. AI-assistance can enhance your delivery of value with the speed and quality your end-users demand. The key is to use assurance-based techniques.
To bring AI into your enterprise usually takes integrating AI governance into your enterprise strategy and understanding the implications.
We’ll then pivot from viewing AI as merely a tool today to envisioning a future filled with AI agents as team members. By the end of this session, you’ll have a solid understanding of both the benefits and challenges of applying AI to the SDLC. Plus, you’ll walk away with practical knowledge to start safely leveraging GAI to build and deliver software. This session is ideal for software architects, engineers, developers, project managers, and technical leaders eager to explore the cutting-edge applications of AI in Software Engineering.
The rapid development of GAI tools offers groundbreaking potential for software engineering, but teams face hurdles in adopting these technologies. This session is essential for software architect, engineers, developers, DevOps professionals, and security leaders aiming to enhance efficiency and quality in their software engineering processes using GAI.
Content:
- Current Practices: Overview of the existing landscape and applications of GAI in software engineering. Where does it make the most sense for your organization’s unique context?
- Adoption Challenges: Key barriers including siloed task execution, lack of cohesive tool integration, and quality/security concerns. What approach should you take to mitigate the risks?
- Human-Machine Collaboration: Strategies for effective collaboration, emphasizing the importance of calibrated trust and team communication. What is the right mix of humans-in-the-loop?
- Future Directions: Insights into the future of GAI in software engineering and emerging leading practices.
Learning Goal: Attendees will gain practical insights into the current state of GAI in software engineering, learn strategies to overcome adoption barriers, and understand future trends to integrate GAI effectively into their software engineering and DevSecOps practices.
Attendees will also walk away with a call to action and materials to immediately leverage with the their teams and organizations.