These days, everyone is trying to improve release frequencies. Under mounting pressure to deliver more, many software teams are considering new strategies to enhance their developer velocity, like AI-assisted code development and platform engineering. AI has been a boon for automatic code generation, but all the code in the world doesn’t matter if you can’t deploy it into production efficiently.
Another point is that velocity isn’t always measured productively, said Dori Exterman, chief evangelist of Incredibuild and a self-proclaimed ‘productivity freak.’ Too often, managers place value on quantitative elements, such as points per sprint and unrealistic metrics, when they really should be considering the quality of these components and how easy they are to maintain in the long run.
I recently chatted with Exterman and other experts about tactics organizations can use to enhance their development velocity. Below, we’ll consider some helpful methods, from optimizing testing automation to creating internal development platforms (IDPs) and evolving your CI/CD processes. Essentially, optimizing the plumbing will be necessary to match the productivity of instant code generation—otherwise, the vision of continual release will remain stunted.
New AI, Same Plumbing…
No tech article today can mention developer productivity enhancements without a nod to AI. Just take the interest in Copilot, for example. Among GitHub Copilot users, 40% of the code they’re checking in is now AI-generated and unmodified, according to a Microsoft executive. And GitHub reported that 88% of Copilot users feel more productive when using the tool.
In contrast to past hype cycles, the latest AI wave is not just a passing fad—it will have long-lasting effects, said Exterman. However, most of our current DevOps processes were constructed for manual code writing, not for the era of AI-assisted development.
If you quadruple the amount of water you use but use the same plumbing system, you can’t push more water through, said Exterman. Similarly, if you amplify the amount of code you’re producing but don’t evolve your build processes, staging and testing, then DevOps suddenly becomes the new blocker.
Ways to Accelerate Developer Velocity
So, how can we improve development efficiency and productivity in the age of AI? Here are some actionable strategies for overcoming these bottlenecks and improving developer velocity.
Accelerating the Plumbing
Exterman stresses the importance of leveraging AI to enhance the foundational processes of development. This involves using techniques such as distributed computing and smart test selection to streamline build and testing processes. By optimizing these foundational steps, development teams can ensure that their coding efforts translate into efficient and rapid progress.
Optimizing Testing through AI
AI’s potential not only lies in code generation—it extends to optimizing testing processes. Exterman suggests tools like Launchable that allow AI to prioritize tests that matter most. This strategic approach to testing enables teams to focus on high-impact areas while maintaining comprehensive test coverage.
Embracing Test Automation
Exterman underscores the importance of comprehensive test automation. Rapid iteration loops are essential for maintaining velocity, and automation facilitates fast feedback cycles. This approach ensures that each code change undergoes rigorous quality checks, preventing errors from propagating downstream.
Keeping an Eye on Technical Debt
One of the potential downsides of increasing programming velocity is the accumulation of more technical debt. As more development occurs, the temptation to compromise on documentation, unit testing and best practices grows. Exterman emphasizes that velocity must be pursued in tandem with managing technical debt to ensure the long-term health of projects.
Investing in Platform Engineering
Recently, we’ve witnessed a paradigm shift toward platform engineering. Just over half of IT organizations have already embraced platform engineering, according to the 2023 State of DevOps Report. Exterman emphasizes the need for a robust internal developer platform that supports the seamless transition of code from development to staging and production. For instance, tools like Terraform and Earthly play a pivotal role in establishing testable environments and reproducible builds.
“Bring more data services closer to the developers,” said Venkat Ramakrishnan, VP, product management and engineering, Portworx by Pure Storage. “Choose platforms that provide easy-to-deploy services that developers can consume and provide visibility and control into the underlying costs.”
Observability, according to Exterman, is also important for gauging efficiency. The ability to continuously monitor and log processes enhances the overall development cycle. One report from Splunk even found that 64% report their investment into observability has exceeded their expectations. Tools from New Relic and Datadog, for example, offer insights that tie quality, delivery and productivity together, enabling teams to make informed decisions.
Evolving CI/CD With Continuous Merge
“CI/CD has done wonders to improve the quality and efficiency of the software development toolchain, but it mainly focuses on things that happen after code has been written and reviewed,” said Ori Keren, CEO and co-founder of LinearB. “Continuous merge is a mindset that extends CI/CD practices further upstream to improve the quality and efficiency of the code review process. We’ve reached a point where code review times frequently cause the most significant delays in the software delivery process; continuous merge is how we fix that.”
Using Cloud-Agnostic Platforms
Cloud-native development has become more popular as organizations seek to design software architectures bred for the natural scalability of the cloud. Simultaneously, both technology and business motivators are resulting in a multi-cloud condition. In this scenario, IT really needs agnostic, open platforms to retain flexibility and reduce bottlenecks.
“Enable developers to build their apps in a cloud-agnostic way so they can scale their apps globally,” said Ramakrishnan. “Much of developer and app momentum is lost when scaling apps. Helping the application teams architect it right by providing cloud-agnostic application platforms like Kubernetes enables developers to move faster, scale faster and reach their customers everywhere.”
Measuring Developer Velocity
The above tips should help improve velocity, but how exactly should you measure success? Well, measuring developer velocity goes beyond simply tracking the number of points completed in a sprint, reiterated Exterman. He emphasizes the importance of focusing on the ability to deliver high-quality products to the market swiftly. He underscores the need for a fast feedback loop for addressing bugs and iterating quickly. AI plays a role here, too, aiding in test writing and enabling continuous delivery.
End Benefits of Improved Developer Velocity
Exterman concludes by highlighting the real-world benefits of increased developer velocity. Faster bug-fixing and release cycles, particularly crucial in industries like gaming, result in improved user experiences and customer satisfaction. He emphasized the significance of aligning key performance indicators (KPIs) and objectives and key results (OKRs) with the aspects that truly matter, promoting a culture of measured and sustainable progress.