Software engineering is much more than coding skills. Elite development teams aren't better only because of their technical skills. Their discipline and ability to balance speed with quality are also key factors in their success. The cumulative experience of thousands of developers has shown that the notion that speed vs quality is a tradeoff doesn’t apply to software development–you can't sacrifice one for the other. Become an Elite Team With Dora Metrics Organizations have been attempting to measure software development productivity for decades. Too often, attention has concentrated on easily-quantifiable metrics like person hours, lines of code committed, function points, or features added per sprint. Sadly, none of these have proven adequate at predicting team productivity. It’s such a complex problem that some have . declared it impossible to solve Despite these failed attempts, DORA set out to establish measures of development productivity. What is DORA? DORA (DevOps Research and Assessment) is a research team founded in 2015 by Nicole Forsgren, Jez Humble, and Gene Kim. The group's aim is to find ways to improve software development. Over the course of seven years, they surveyed thousands of software professionals across hundreds of organizations in various industries. The team's findings were first published in . The book introduced four benchmarks that correlate to high-performing organizations: the DORA metrics. Accelerate: The Science of Lean Software and DevOps (2018) In the same year that the aforementioned book was published, Google acquired the group and established the , which is responsible for publishing the yearly . DORA research program State of DevOps Report What are the DORA Metrics? The groundbreaking insight obtained by DORA’s research was that, given a long-enough term, there is no tradeoff between speed and quality. In other words, reducing quality does not yield a quicker development cycle in the long run. Both speed and stability are essential. Focusing on adding features at the expense of quality results in substandard code, unstable releases and technical debt, eventually stifling progress. DORA identified two key aspects of software development: Throughput measures the time it takes new code to reach production. Stability: measures how often a deployment fails and the average time to fix. DORA measures stability with two metrics: Time to restore service (MTTR): how long it takes the organization (on average) to recover from a failure in production. Change fail rate (CFR): the percentage of releases or deployments causing a failure in production. On the throughput side, DORA adds two more metrics: Deployment frequency (DF): how often the organization successfully releases the product to the users or deploys it to production. Lead time to changes (LT): the amount of time it takes a commit to reach production or release DORA measures two aspects of software development: stability and throughput. The DORA metrics are measured using the following 4 levels: Elite, High, Medium, and Low. Metric Low Medium High Elite Deployment frequency fewer than 1 per 6 months 1 per month to 1 per 6 months 1 per week to 1 per month On demand (multiple deploys per day) Lead time for changes more than 6 months 1 month to 6 months 1 day to 1 week Less than 1 hour Time to restore service more than 6 months 1 day to 1 week Less than a day Less than 1 hour Change fail rate 16 to 30% 16 to 30% 16 to 30% 0 to 15% According to the , the performance across the different levels is dramatic. State of DevOps Report 2021 (PDF) Elite teams are thousands of times more productive than the rest. 💡 In 2021, the DORA team introduced a fifth metric: reliability, which measures operational performance by rating how organizations meet or exceed their reliability targets. Achieving elite level with DORA metrics Let me preface this section with a few warnings: DORA metrics are only valid in tracking a team's progress in their DevOps journey. They cannot be used to compare teams or individuals. Goals and metrics should not be confused. Since all metrics can be gamed, equating metrics to objectives leads to perverse incentives. If, for instance, management decides to optimize deployment frequency no matter the cost, it will be in the team’s best interest to deploy any tiny change they can think of, regardless if it adds value to the user or not. Organizational culture has an enormous impact on team performance. We'll come back to this later in the post. Okay, now that that’s out of the way, let’s carry on. Elite teams have been growing in number in the last few years. In 2018, only 7% of teams or organizations were considered elite. In 2021, that number grew to 26%. So we know growth is achievable. DevOps maturation of the software industry measured in DORA metrics. Source: State of DevOps Report 2021. The question is how to use DORA metrics to step up a team’s or organization’s game. A bad value in a metric is a symptom. It indicates there are organizational, cultural, or skill problems to address. Once these are managed, the underlying metric should naturally improve. Improving throughput Organizations with slow production cycles have low deployment frequency and high lead time for changes. Often, we can improve throughput by optimizing (CI/CD), identifying organizational problems, speeding up test suites, and reducing deployment friction. continuous integration and continuous delivery Ask yourself, “what’s preventing me from releasing right now?” The answer will reveal bottlenecks in the organization. For example, you may be wasting too much time in code reviews, waiting for QA to approve changes, or holding a release until other teams finish their features. You can improve throughput in a number of ways: Reduce the size of your releases. Ship small, safe changes frequently. If a feature is not ready for prime time, release it hidden behind a or with a dark launch. feature flag Ensure that the entire deployment process is automated and can be done at the press of a button. That means no checklists and no manual interventions during deployment. Adopt . It will reduce the chance of merge conflicts and encourage collaboration. trunk-based development Optimize the speed of continuous integration by and removing . managing slow tests flaky tests Track how long you spend on each step in the software delivery process. Examine the cycle time and figure out where you can save time. Improving the project’s stability Speed without stability ultimately leads to accruing technical debt and spending more time fixing bugs than shipping features. When the stability metrics don’t look good, users have a bad experience and developers spend most of their time extinguishing fires rather than coding. Here are a few ideas you can follow to improve stability: Implement code quality checks in the . Refuse to ship code that is not up to par. CI pipeline Reinforce the code peer review process or experiment with pair programming. When disaster strikes, focus on recovery over all other tasks. Ensure you have enough monitoring and observability in your system to quickly determine the cause of a failure. Run a “lessons learned” meeting every time a severe outage occurs. Use in your deployment pipelines to avoid deploying in a faulty environment. smoke testing Implement automated rollback to your deployment. Experiment with or deployments. canary blue-green The generative culture Perhaps unsurprisingly, the 2021 State of DevOps report found a high correlation between elite teams and a generative culture within the organization. The term was coined by Ron Westrum to describe a culture that's inclusive, highly cooperative, and that provides the psychological safety needed to take risks without fear of fallout. "generative culture" The culture of the organization goes beyond a single team. It must be propped up by management, shared by the engineering team, and maintained throughout the company. A generative culture brings siloes down, encouraging collaboration beyond the engineering teams. Pathological(Power oriented) Bureaucratic(Rule oriented) Generative(Performance oriented) Low cooperation Modest cooperation High cooperation Messengers "shot" Messengers neglected Messengers trained Responsibilities shirked Narrow responsibilities Risks are shared Bridging discouraged Bridging tolerated Bridging encouraged Failure leads to scapegoating Failure leads to justice Failure leads to inquiry Novelty crushed Novelty leads to problems Novelty implemented Source: , Dr. Ron Westrum, 2004. A typology of organizational cultures Conclusion It would be a mistake to think that improving DORA metrics automatically makes for better teams. It's the other way around: an inclusive, generative culture naturally yields higher benchmarks. In other words, there is no chance of sustaining an elite team in a low-cooperation, risk-averse environment. Setting metrics as goals is not only near-sighted, but often an indicator that an organization has strayed into the pathological or bureaucratic culture. Would you like to know what your DORA score is? Take the and find out! DevOps Quick Check Also Published Here

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