Modern software development rarely starts from a blank page. Instead, applications are assembled from a vast ecosystem of frameworks, libraries, and open-source components that accelerate development and enable teams to build complex systems quickly. In many cases, the majority of an application’s functionality comes not from code written internally, but from external dependencies - and lately, AI. This shift has dramatically improved developer productivity, but it has also i

Modern software delivery has transformed how quickly organizations can build and release software. Continuous integration and continuous delivery pipelines enable teams to deploy updates frequently, respond rapidly to customer needs, and iterate faster than ever before. However, this speed can expose a major weakness in traditional security models. Historically, security checks were performed as a final gate before release. Security specialists would review code, run vulnerab

We’ve explored a range of approaches to measuring software delivery and ensuring teams build effectively in my recent posts. From well-established engineering and testing practices to modern delivery metrics. The metrics we’ll explore today, however, are far newer, emerging in response to the rapidly evolving role of AI in the software development lifecycle. As AI becomes embedded in the software development process, from code generation to testing and documentation, engineer

In my recent posts, we’ve looked at several ways of using metrics to help understand the delivery of teams across different aspects. Looking retrospectively is a great way of identifying ways to improve – but you also want to use your data to help you be more proactive and identify ways to better plan and predict the outcomes of your different delivery efforts. In software delivery, speed and quality mean little if outcomes are unpredictable. Leaders and stakeholders need to

In my previous blog post , I spoke about the importance of measuring flow metrics and how they can unlock blockers in your development practices and help you deliver more quickly. However, while speed and flow get software into production, its quality and stability determine whether it stays there . High-performing engineering teams don’t optimise for velocity alone; they balance it with reliability, resilience, and customer trust. Quality and stability metrics reveal how sa

I started writing in my previous blog post about the importance of metrics and how they provide detailed insight into the development life cycle and identify key areas for improvement in software delivery for teams. Before delving into the different metrics themselves, though, it’s important to look at how we actually gather the data in the first place. After all, there is no way we can gain insight into our delivery if we don’t have access to the data that provides it. How

The software world may be looking to always deliver innovation and new features faster. However, delivery speed alone is no longer a meaningful indicator of success. Teams are expected to deliver quickly and safely, sustainably, and predictably. Doing so though is a big challenge for many teams and the key to this improvement is understanding your software well and knowing what is going in in your software delivery process very well. This is where metrics play a critical rol

As the industry continues to rapidly adopt Large Language Models (LLMs) to power chatbots, copilots, search, automation, and decision support, one discipline has emerged as a critical success factor: prompt engineering. While LLMs are incredibly powerful, their outputs are highly sensitive to how they are instructed. The difference between a vague, unreliable response and a precise, trustworthy one often comes down to the quality of the prompt. Soemtihng which is a completely

In my previous post , we explored the big changes required in architecture to make LLMs successful. As those who are familiar with me will know that quality and testing are vital aspects of software architecture to me. So, I feel like I wouldn’t be able to explore the topic of software architecture without aspects of testing and quality. Testing and QA Must Be Built Into Architecture In classical software engineering – at least at a fundamental code level - testing often tend

I’ve spent the past two blog posts talking about the importance of change communication , particularly the transparency required to...

In my previous blog post I spoke about change management and why communication is the most important part of managing change. Change in...

Why Change Communication Matters More Than Ever I’m going to change tack a little and move away from some of my technical articles and...

In my previous post, I started to look at modernizing security, starting with software architecture. However, when looking at secure...

In my previous blog post , I explored the concept of Green IT through the lens of development practices, highlighting the importance of...

As organizations modernize their software systems, there's an increasing responsibility to do so sustainably. Green IT emphasizes the...

In my last post, I started looking at the technical steps for preparation of technical modernization. It was a lot to cover, so I split...

In this series, we’ve looked at what to look at when approaching modernization and the importance of approaching modernization from a...

This is the second of a multi-part blog series on modernization. In the first post, I discussed what needs to be modernized and the...

When it comes to performing technical work—such as designing, building, and testing software—the primary focus should generally be on an...

I’ve spent the last two blog posts (here and here) focusing on the importance of requirements in software delivery. Something very...