Key SDLC Performance Metrics for Engineering Leaders
Engineering leaders must have an effective system in place to measure their team’s performance and ensure that they are meeting their goals. One way to do this is by monitoring Continuous Deployment Key Performance Indicators (KPIs).
CD and Automated Tests
If you’re not aware, Continuous Deployment, or CD, is the process of automatically deploying software updates into production after successful tests (and as close to the time it was committed as possible). Automated tests are needed to build confidence and trust that these new features in each commit won’t break production.
By doing this, engineering teams can quickly and efficiently iterate their products without manual intervention. Continuous Deployment isn’t just a buzzword: There are concrete business reasons and important ways to judge whether or not your Continuous Deployment solution is helping you reach the business objectives that led you to CD in the first place.
Knowing the most important Continuous Deployment KPIs to track for the process is essential to understanding how it’s performing, what needs improvement and where resources should be allocated.
The DORA metrics (Lead Time, Change Failure Rate, Time to Restore Service and Deployment Frequency) are the most common metrics used to measure a team’s Continuous Deployment performance.
Continuous Deployment done well is data-driven. Data drives decisions about what to work on and how to work on it, and it tells us whether the changes are working properly and whether things are improving. The SDLC embodies these questions. Without data, decision makers are stuck between subjective feelings and objective truths.
The leading voices in Continuous Deployment answer these questions in Accelerate by explaining the science at work in IT performance. Accelerate, like the DORA metrics, distills it down to four KPIs: lead time, deployment frequency, change fail percentage, and time to restore service. Armory Continuous Deployment solutions put these KPIs into your hands. In this post, we explain how the KPIs relate to the SLDC.
Lead Time for Changes aka Time to Market
Deployment is a measure of how many features and fixes are being completed within a given time frame. Each deployment contains features and fixes that have been added to the product since the last deployment.
Lead Time measures how long it takes from when feature development begins to when it is deployed to customers. Measuring lead time requires combining two ends of the deployment: source control and the deployment process. The clock starts when an engineer begins to write code, but the real value comes when the change has passed through CI. The clock stops when that code passes through the production deployment. Technically, this requires tracking commit data through all of the deployment process.
Innovations in development practices are not just about shipping better software—it’s about getting features in front of your customers that give your business a competitive advantage, and doing so as quickly as possible. Continuous Deployment should dramatically speed up time to market and time to value. Measuring the amount of time it takes from determining a business logic for a specific application or new feature to deploying that app or feature to your customer base is the ultimate measure of how well your continuous deployment solution is working.
Change Failure Rate
Change Failure Rate tracks the percentage of changes that fail in production. Accurately measuring change fail percentage requires uniting the deployment pipeline and external monitoring systems. The deployment pipelines capture technical failures (e.g., infrastructure issues) and planned failures (e.g., canary stages). This is enough for an approximate measurement. This may be enough for some teams, but it does not account for failures while running production.
Deployment Frequency
Deployment Frequency measures how often your engineering team is shipping changes to customers. The frequency may be checked in any desired time interval, such as monthly, weekly, or daily. Deployment events contain metadata about the entire lifecycle so fine-grained measurements like deployments per day, per developer, per environment are possible.
When truly practicing Continuous Deployment, deployments will likely happen multiple times per day. The deployment process should be automated and repeatable, to ensure the following:
- There is zero downtime during deployments
- Deployments to test environments are handled exactly the same way as deployments to production environments
- Developers are not wasting time managing the deployment rather than working on improvements and new capability
- It’s safe and easy to roll back a deployment if it’s clear that something is wrong
It’s impossible to speed up the actual deployment process—and ensure it is done safely and repeatably every time—if you’re deploying manually. Using a Continuous Deployment solution like Armory is the only way to make sure you’re handling deployments quickly and safely.
Time to Restore Service
Mean Time to Restore Service (MTTR) is a measure of how quickly a service is restored after an interruption or outage. This metric is important for businesses and organizations because it helps them understand the impact of service disruptions on their customers and identifies areas for improvement.
One of the main benefits of monitoring MTTR is that it can help organizations identify and address the underlying causes of service disruptions. For example, if MTTR is consistently high for a particular service, it may indicate a problem with the service’s design or infrastructure, or a lack of proper incident management procedures. By addressing these issues, organizations can improve their MTTR and reduce the impact of service disruptions on their customers.
Bugs—Do You Have Them and How Quickly Are They Fixed?
Ok so this isn’t an official DORA metric but let’s not forgot about squashing bugs and the quality of software. Move fast and break things, right? Continuous Deployment lets you move fast without breaking things.
Part of Continuous Deployment is recognizing that mistakes happen and things break. But a successful process and solution includes a sufficiently robust quality process that customers aren’t the ones discovering bugs in your software. It also involves testing new releases in an environment that is identical to your production environment, and managing deployments in a repeatable way, so you know that code is deployed to test and production environments in exactly the same way.
Track how many bugs are caught during testing vs. how many are caught in production—sometimes called your defect escape rate. There are a couple other important bug-related metrics: How long does it take to fix bugs in production, when they happen? How often is a defect so serious it crashes the application?
Conclusion
Common SDLC metrics also include development time and cost, project completion rate, defect density, code quality, and customer satisfaction. These metrics are used to track progress at each stage of the project and provide an overall view of the project’s progress.
By monitoring these metrics closely, engineering leaders can gain a better understanding of the effectiveness and efficiency of their Continuous Deployment process and overall performance. These KPIs provide insights into how well the engineering team is working together, whether problems are occurring in development or testing and if customer feedback is being taken into account when deploying changes. They can also help identify areas for improvement and ensure that the team is meeting their goals.
Ultimately, engineering leaders should use these performance metrics to make data-driven decisions on how best to allocate resources, prioritize tasks, and optimize their processes. Knowing your team’s KPIs can help engineering leaders stay focused on the big picture while still keeping an eye on the smaller details, ensuring that their teams are working together efficiently and effectively to create products that customers love.
With this knowledge, they can make more informed decisions that will help their teams succeed and reach their goals and create successful products.
