The advent of Generative AI heralds a transformative era in the Software Development Life Cycle (SDLC), offering a plenty of opportunities to augment the processes of requirement analysis, design, development, testing, deployment, and monitoring. These enhancements come with the promise of consistently elevated quality and efficiency.

Despite the immediate gains achieved by harnessing the low-hanging fruits of Generative AI, the journey often presents its own set of challenges. These arise particularly when attempting to apply AI solutions to complex, unique, and comprehensive problem sets. To navigate these challenges, a sophisticated engineering approach is required—one that effectively harnesses the capabilities of AI and orchestrate it to address bigger problems end to end.

In this session, I would like to share insights and experiences gained from integrating Generative AI into the SDLC to significantly improve both quality and efficiency. The discussion will focus on several key aspects, including:

– Utilizing AI to extract actionable quality insights regarding products and systems.

– Implementing AI-driven code and test reviews to learn from historical defects.

– Enhancing testing protocols with AI Testers.

– Advancing intelligent problem monitoring for proactive issue resolution.

Takeaways from the talk:

• Understanding the Role of AI in SDLCPractical Insights in elevating all aspects of quality (requirement, coding, test, operation, support)

  Overcoming Challenges