Agile

In Agile software development, the Definition of Done (DoD) is critical for ensuring that teams share a clear understanding of when a task or user story is fully complete. It’s not just about checking boxes but ensuring the deliverable meets certain quality standards, is testable, and can be released into production with confidence. A solid DoD serves as a benchmark for delivering high-quality software that aligns with both customer expectations and regulatory requirements. Yet, despite its importance, defining and managing a robust DoD can be challenging.

In Agile software development, Acceptance Criteria play a crucial role in defining the conditions under which a user story or feature is considered complete and functional. These criteria act as a shared understanding between stakeholders and development teams, outlining the expected behavior of the system under different conditions. Well-written acceptance criteria provide clarity, prevent scope creep, and make testing more straightforward.

If you’ve been following along with my recent posts, you know I’ve talked a lot about how AI can help write user stories, manage cybersecurity, and drive agile workflows. But let’s take a step back for a moment—what about looking backward instead of always pushing forward? This is where AI-powered retrospectives come into play.

When developing software, user stories are crucial for translating high-level requirements into actionable tasks for development teams. These stories serve as a bridge between stakeholders and developers, ensuring everyone is aligned on what needs to be built and why. Traditionally, creating user stories has been a manual and often time-consuming process. However, with the advent of artificial intelligence, this task can now be streamlined, enhancing efficiency and accuracy. In this first installment of our three-part series, we will explore how to prepare AI to generate user stories from requirements documents.

As agile teams, we’re constantly juggling multiple tasks, learning new technologies, and adapting to changing requirements. But have you ever stopped to consider how all this mental juggling affects our productivity and effectiveness? Enter Cognitive Load Theory, a concept that’s becoming increasingly relevant in the world of software development.

As an agile consultant, I’ve seen countless teams grapple with the concepts of throughput and goodput. These terms often pop up in discussions about team performance and project outcomes, but there’s often confusion about what they really mean and why they matter.