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.

There’s something thrilling about autonomy. The idea of a machine, not simply a tool to be used, but a participant in the decision-making process of a complex system like project management, carries with it a mix of awe and uncertainty. It’s not about replacing human roles but rather augmenting them—allowing artificial intelligence to take on an agentic role where it acts with a degree of independence and adaptability. Welcome to the world of agentic AI.

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.

Imagine this scenario: You’re deep into an Agile project, racing towards your next milestone. Amidst the flurry of sticky notes, stand-up meetings, and code reviews, a crucial question arises: “Didn’t we tackle a similar challenge last month?” The memory of a discussion lingers, but the specifics are hazy, and documentation is nowhere to be found. This situation is common in many Agile teams.

As we continue to integrate AI into the process of software development, it’s essential to look beyond just writing user stories. While user stories are fundamental in defining the “what” of a project, there are tools and techniques that can greatly enhance our understanding of the “how.” This post will explore how Gherkin, sequence diagrams, and Mermaid notation can be used in conjunction with AI to bring greater clarity to functional requirements and streamline the process of automated testing. By leveraging these tools, we can create a more comprehensive and actionable set of specifications that bridge the gap between high-level user stories and detailed technical implementations. This approach not only enhances communication between stakeholders but also paves the way for more efficient development and testing processes.

In the first part of our series on writing user stories with AI, we explored the foundational steps to prepare an AI, such as ChatGPT, to write user stories that meet the needs of an Agile development team. Now, it’s time to dive deeper into the next critical step: crafting and fine-tuning your prompt. A well-constructed prompt is the backbone of generating high-quality user stories. It provides the AI with the necessary context, guiding it to produce user stories that are clear, detailed, and actionable.

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.