One of the most often discussed code efficiency indicators in the software development industry is the DORA metrics.
Even while many engineering teams are aware that DORA is the answer, the majority of them have trouble putting DORA to the proper use. Let’s examine why DORA is important to developers in this blog post as well as how to utilize and avoid abusing DORA metrics to evaluate the status of projects and the software development lifecycle.
What are DORA Metrics?
Engineering leaders can measure throughput (velocity), quality, and stability of software delivery using DORA, a mix of four DevOps indicators. Briefly stated, the metrics are:
Frequency of deployment: The rate at which new software is released and production-ready code is updated.
Change lead time: The total amount of time it takes from the moment a change request is made until it is implemented, then reaches the client.
Failure rate for changes: The percentage of deployments, rollbacks, and patch-fixers that were unsuccessful
Mean time to recovery (MTTR): The amount of time needed to resume regular operations after outages, events, disasters, etc.
In their most recent State of DevOps report, Google has now introduced Reliability as the sixth DORA indicator to evaluate operational success. Cycle time is another benchmark that the majority of engineering managers want to combine for more precise insights.
Why should developers care about DORA metrics?
Why are DORA measurements being used by so many teams and organizations instead? I can think of six main justifications.
Research supports these, demonstrating a statistically significant relationship between favorable DORA measures and favorable organizational performance. DORA measures are not based on intuition.
The DevOps practices we have been using for many years have been crystallized into DORA metrics in a clear and concise manner. The DORA measurements demonstrate how well your team is pursuing lifelong learning and improvement. We learned, for instance, via experience that batch size reduction helped us do our task more rapidly. Deploy frequency, change lead time, change failure rate, and mean time to recovery were the metrics DORA used to group these factors into groups and demonstrate how they interrelate. From the perspective of a practitioner, DORA measurements have given names to the actions we have always taken.
DORA measurements maintain simplicity. Choosing what to measure in terms of engineering can be difficult for organizations. Teams can start with measurements that have the support of the audience and are properly defined with industry benchmarks thanks to DORA.
Because DORA measurements are team measures, they don’t instill in developers the same anxieties and phobias that individual metrics do. Although DORA metrics acknowledge that software development is a team sport, they can nonetheless be used as weapons. The State of DevOps and DORA reports are all about teams, if you read them.
DORA metrics reduce complicated processes to clear, precise measurements. They can create four main measurements using information from source control, source review systems, issue trackers, incident management providers, and metrics tools. This enables comparison of DORA measurements between teams, despite the fact that no two teams are same. Based on their performance across the four major criteria described above, teams can classify themselves according to the DORA research into low, medium, and high performance categories. Teams can use this to make broad judgements about their performance relative to other teams.
A wide range of DORA measures are covered, including the development process and how well it serves consumers. The process from the moment a developer begins writing code until the moment the team sends anything to production is examined using DORA metrics. They understand that no one wants to follow the “move quickly and break stuff” strategy. DORA metrics promote the “move fast, responsibly” strategy as being the healthiest one.
The advantages of measuring DevOps performance with DORA metrics
Software development is a science- and data-driven process by nature, yet there are many abstract processes that are hard to quantify. That was the main driving force for the DORA team’s efforts to explain these measures.
By measuring these measures,
- the process becomes more stable and tangible. Organizations are better able to pinpoint areas for improvement.
- It streamlines their development processes when they monitor DORA indicators.
- Teams eventually produce quicker, higher-quality software as a consequence, which is essential for today’s performance-driven businesses.
- Data-driven decision making, improved processes, and increased value delivery are further major advantages.
DORA metrics measurement challenges
Although DORA metrics give DevOps teams the much-needed structure they require, adopting them has its drawbacks as well. If many engineering teams wish to measure the four metrics successfully, a cultural and procedural transformation must take place.
Several difficulties to consider are as follows:
- difficult to combine data that is distributed
- Only provided in raw format, difficult to alter data
- Data that has to be converted into units that can be easily measured
Furthermore, it’s critical to position each of the DORA measures in the appropriate context. The Deployment Frequency should only be used in the appropriate circumstances since, as we’ve already discussed, it won’t truly speak to the quality or stability of the code.
Bottom Line
No collection of metrics, including DORA, is the magic wand that will make your engineering team the greatest. However, DORA measurements have assisted the software industry in uniting around a methodical approach to gauging operational efficiency and product delivery that genuinely appeals to engineers. They could even like it.