DORA (DevOps Research and Assessment) Metrics have become essential in organizations with strong engineering practices because they provide crucial insights into their software development and delivery efficiency. These metrics are:
- Deployment Frequency: Measures how often code changes are deployed to production. High deployment frequency indicates a rapid and efficient release process.
- Lead Time for Changes: Calculates the time it takes for code changes to move from idea to production. Short lead times signify swift development and deployment cycles.
- Mean Time to Recover (MTTR): Evaluates the average time it takes to restore services after a failure or incident. A low MTTR indicates robust recovery capabilities.
- Change Failure Rate: Tracks the proportion of code deployments that result in issues or require rollback. A low change failure rate reflects the stability and reliability of the development process.
However, DORA Metrics are not enough because they only focus on the outcome of the software development process: the deployment. And for managing organizations that rely on software, you need more information and data on the activities that happened before that. That’s why good Engineering Managers need a system view approach. That’s what some people are referring to as Engineering Ops.
That means tracking performance, collaboration, process and quality metrics across the entire software development life cycle. So tracking metrics from the ideation, design, development and testing stages, to the deployment process, allows the teams to make smart decisions that align with the organization’s goals.
A system view approach involves metrics, such as:
- Throughput: I like to measure Throughput in terms of Pull Requests being merged in a given period of time. That’s one of the best proxies for “Productivity” we have as software engineers. Such metrics can enable us to measure if the teams are improving, and help us to spot problems before they can reach deployments.
- Lead Times: DORA focus on Lead Time for Changes. That’s already good, but we’ll also probably want lead times for the steps involved in collaborating and making the software, such as Time to Review, Time to Engage and others.
- Deployment Frequency: Of course, measuring Deployment Frequency is a important part of a healthy engineering organization.
- Code Quality: Problems with code quality will affect every other metric. So, it’s important to keep an eye on Code Coverage, Complexity, Issues reported by Static Code Analysis. Especially on how those metrics will develop over time when you scale up the team.
- People Surveys: Getting data on job satisfaction, work environment, and team dynamics are always difficult to implement, but can give you a lot of insights into how people perceive their organization.
These metrics provide a holistic picture of the software engineering process, providing a better idea of how teams are doing, in addition to metrics related to deployment. This helps Engineering Managers and their teams have a more complete understanding of the team’s performance and make better decisions. These metrics will give a clear understanding of the overall health of the organization and the quality of their software development process.
Software engineering metrics are useful in providing insight into how teams are performing and enabling Engineering Managers to make informed decisions. The use of these metrics allows for informed and proactive management of resources and allows the engineering process to be more efficient and successful. DORA Metrics give you an excellent start, but you’ll also want more if your job is to help your team.
If your team wants to have a more comprehensive data on how your organization collaborates and ships software, take a look into SourceLevel. We can track Deployments, Pull Request, Code Reviews, Throughputs and so on. I’ll be glad to help you.