For software engineering teams that are asked to frequently release features without disruption, measurement is a critical component and missed opportunity. I’m not just talking about measuring KPIs or other engagement metrics, but also tracking performance issues like latency – the measurements that tell you if your features break something or if they’re negatively affecting your database. 

Measurements of this magnitude are most effective at the feature level, and they’re crucial for mitigating risk, ensuring infrastructure quality, and speeding up the velocity of software delivery. 

Feature-level measurement done right also helps promote a healthier work environment. It provides visibility and alignment, eliminates the need for constant hypervigilance, and extends a psychological safety net across the whole software engineering department.

After implementing the right feedback loop with instant guardrail metrics, teams are freed from the ongoing pressure and the time-consuming process of responding to fire drills. Thus, their focus on chasing bugs, rolling back releases, and restoring service can be applied to other aspects of the job that, ideally, bring more satisfaction.

Healthy ways to start measuring your releases 

Secure an easy-to-use feedback loop 

Effective software measurement starts with a feedback loop that’s extremely easy to follow and track, shareable across teams, and cost-effective. Feature management platforms with measurement and learning capabilities are a great way to achieve the instant and actionable feedback loop you need. 

Look for the right platform. The best tools don’t just give you the risk mitigation of feature flags, which provide an easy on/off toggle when deploying or releasing features. They’ll also connect data to every feature flag you release, so you can get insights at the right time to make quick, accurate decisions. Some platforms let you connect to your existing data tools, others include out-of-the-box performance metrics with your subscription. 

Whatever tool you choose, ensure that it’s easy for everyone to access, where data and results can be summarized into easy-to-locate dashboards that are shareable across teams. By making feature-level feedback available to everyone, you can be sure that your engineers will actually use it. 

This might entail an element of training to get teams on board, but once they’re comfortable (and trusting of the process), the productivity and psychological safety it provides is 100% worth it. 

Closely monitor and detect feature-level issues

Understanding any negative outcomes of the software you release is valuable, especially when it comes to making informed decisions that directly affect production services.

By closely monitoring and detecting feature-level impact, engineers can understand if the features they release are making things better or worse. If a feature is reporting negative performance metrics, they can roll it back before the issue spirals out of control. 

Relying on application performance monitoring (APM) tools to do this isn’t enough, because issues are often detected when things are fully rolled out. Before that, things aren’t always loud enough to rise up to the noise of your overall system. 

Conversely, feature management tools with instant feature impact detection can provide monitoring and instant feedback in real-time, early in your rollout process. This is a powerful way to arm your engineering team with measurement and learning capabilities. Not only will they prevent harmful failures by identifying the locations and causes of feature-related issues, but teams won’t have to be so hyper-focused on reacting to bugs, code breaks, and other stressful surprises. 

Master gradual rollouts

Gradual rollouts allow teams to measure the risks against the rewards of new features without making a full commitment to a release yet. By gradually rolling out new updates to a limited subset of users, software engineers can closely monitor performance and engagement metrics to identify positives and minuses early on. This happens in a way that minimizes a large impact across a broader user base. 

Some might see this as a strategy that prolongs the process of shipping features out to every customer, but it definitely saves trouble and time later on. Opening the door for measurement at an early stage of gradual rollout allows teams to fine-tune their work before releasing it to a full customer base. They can detect early performance, load, or quality degradation, and address issues before they affect the wider customer base and the overall production systems. This ensures that when they do get a feature out to everyone, it’s less likely they’ll have to go back and fix things further.

Overall, gradual rollouts mitigate risk while also maximizing the quality of output, making the process a lot less stressful for team members. 

Experiment with old vs. new versions

Experimentation isn’t an exclusive strategy for product teams to try every possibility and add more work to the engineer’s plate. It’s an effective way to compare the quality of an old implementation versus a new one. 

This can be achieved by randomly splitting users into two groups; one group is exposed to the old version while the other experiences the new version. Once a direct comparison of performance and business metrics is made, it’s quick and easy to make informed decisions about whether to fully release the new version. In other words, you’ll know right away if it’s a waste of time to proceed with the new implementation, saving you headaches later on. Finally, when a decision is made, there is no anxiety or second-guessing. 

The impact of feature measurement on engineering culture

Creates a clearly defined finish line

Done doesn’t necessarily mean stable. Getting a feature into production quickly doesn’t automatically ensure that the software you “finish” is free from bugs, performance issues, or other potential problems. And when these issues occur it often means that teams, already rushing to release as fast as possible, have to return to what they initially deemed complete, creating more work to guarantee feature stability.

With measurement capabilities like feature impact detection, it’s easier to ensure the stability of your rollouts. If they’re not stable, you’ll know right away and you can just roll them back to make the changes you need. Once stability is reached, you can finally release the product more broadly and move on to the next project without having to retrace your steps later on. 

This way of working certifies a finish line that is a little more concrete, helping to improve engineer productivity and happiness. Nobody likes to go back to the drawing board, and with measurement, it is a little less likely. 

Ignites continuous improvement

Regularly measuring feature performance allows engineering teams to iterate and improve their work. By tracking metrics like user engagement, conversion rates, or system performance, teams can identify areas for optimization and implement targeted enhancements.

We walk many more miles taking smaller steps rather than attempting giant leaps. This iterative process not only leads to more effective and efficient features but also boosts team morale as they see tangible progress and the positive impact of their work.

Collaboration and alignment

Feature measurement fosters collaboration by providing a shared understanding of product performance across different teams. When engineering, product, and design teams have access to the same data, they can align their efforts, set collective goals, and work together towards common objectives. 

With this approach, every team will care about your site performance and its quality just as much as your performance team. This shared context promotes effective communication, minimizes misunderstandings, and reduces silos, leading to more harmonious and productive teamwork.

Recognition and validation 

Feature measurement allows engineering teams to track the success of their work and receive recognition for their contributions. Clear and measurable metrics provide tangible evidence of the value they bring to the product and its users. 

This validation enhances team morale, job satisfaction, and overall happiness, creating a positive work environment that encourages motivation and continued dedication.

Final Thoughts

According to a recent survey of software professionals, 1 in 10 devs know someone who lost their job due to a failed release. In my opinion, release failures shouldn’t lead to job terminations. Failures should be the window into valuable lessons learned. Effectively implemented measurement allows for such failures to become learning curves. 

Knowing the impact of every feature you release is the ultimate psychological safety net. Rather than holding on for dear life, engineers are allowed to thrive. They will have the confidence and autonomy to make fast, data-driven decisions. They’ll be more aligned to key business goals and big-picture objectives. But first and foremost, they won’t be distracted by the anxiety that comes with reacting to change-related issues.