Engineering Productivity: how to measure and improve it
Engineering Metrics
Oct 9, 2023
In the realm of software engineering, productivity isn't just about producing code; it's about producing quality code efficiently. As engineering teams grow and projects become more complex, the need for clear metrics to measure and improve engineering productivity becomes paramount. Let's delve into the intricacies of engineering productivity and how to optimize it.
The Essence of Metrics in Software Engineering
Metrics serve as the compass for any engineering organization. They provide a quantifiable measure of the engineering team's performance, from the speed of software development to the quality of code produced. Metrics like cycle time, lead time, and lines of code offer invaluable insights into the efficiency of the development process.
However, while metrics like DORA (Deployment Frequency, Lead Time for Changes, Time to Restore Service, and Change Failure Rate) are essential in assessing the overall health of an engineering organization, they often don't dive deep enough to pinpoint specific bottlenecks or areas for improvement.
Beyond DORA: The Need for Deeper Insights
DORA metrics provide a broad overview of an engineering organization's health. They highlight how often code is deployed, how long it takes for changes to be implemented, and the reliability of those changes. However, they often fall short in providing granular insights at the repo, team, or individual level.
For instance, while DORA metrics might indicate that the cycle time is longer than desired, they won't necessarily pinpoint whether the delay is due to extended code review processes, stale pull requests, or challenges in the workflow of specific development teams.
Optimizing Engineering Productivity
1. Granular Insights: To truly optimize engineering productivity, leaders need insights at a more granular level. This includes understanding code velocity and quality metrics at the repo, team, or individual level. Such insights can help pinpoint specific bottlenecks and areas for improvement.
2. Continuous Feedback: Regular feedback from team members and stakeholders can help identify challenges early on. Whether it's challenges in the review process, issues with specific frameworks, or inefficiencies in the workflow, timely feedback can lead to timely interventions.
3. Skill Development: Ensuring that the engineering team is equipped with the latest tools and frameworks can significantly enhance productivity. Regular training sessions, workshops, and skill development initiatives can keep the team updated and efficient.
4. Automation: Incorporating automation, especially in areas like code testing and deployment, can significantly reduce manual intervention, leading to faster and more efficient processes.
The Role of Tools in Enhancing Engineering Productivity
While understanding the importance of metrics and implementing strategies to optimize productivity is crucial, the right tools can amplify these efforts. BuildPulse Engineering Metics is one such tool that is revolutionizing the way engineering teams approach productivity.
Unlike traditional tools that focus primarily on DORA metrics, BuildPulse dives deeper. It offers insights into code velocity and quality metrics at the repo, team, or individual level. With features like a co-pilot that notifies developers on Slack about estimated PR review times, stale pull requests, and stale reviews, it ensures that the team remains agile and efficient.
Moreover, with its intuitive dashboards and actionable insights, engineering managers can swiftly identify bottlenecks, allocate resources judiciously, and implement changes to bolster team productivity.
Conclusion
In the ever-evolving world of software engineering, productivity is the key to success. It's not just about producing code but producing quality code efficiently and consistently. By leveraging metrics, diving deeper than traditional DORA metrics, and utilizing tools like BuildPulse Engineering Metics, engineering teams can navigate the challenges of software development with finesse.
Incorporating tools that provide real-time insights, like BuildPulse, can be the difference between a project that's delivered on time with high code quality and one that faces delays and challenges. As the adage goes, "What gets measured gets managed," and in the world of software engineering, every metric counts.
FAQ
What is the difference between a flaky test and a false positive?
A false positive is a test failure in your test suite due to an actual error in the code being executed, or a mismatch in what the test expects from the code.
A flaky test is when you have conflicting test results for the same code. For example, while running tests if you see that a test fails and passes, but the code hasn’t changed, then it’s a flaky test. There’s many causes of flakiness.
What is an example of a flaky test?
An example can be seen in growing test suites - when pull request builds fail for changes you haven’t made. Put differently, when you see a test pass and fail without any code change. These failed tests are flaky tests.
What are common causes of flakiness?
Broken assumptions in test automation and development process can introduce flaky tests - for example, if test data is shared between different tests whether asynchronous, high concurrency, or sequential, the results of one test can affect another.
Poorly written test code can also be a factor. Improper polling, race conditions, improper event dependency handling, shared test data, or timeout handling for network requests or page loads. Any of these can lead to flaky test failures and test flakiness.
End-to-end tests that rely on internal API uptime can cause test flakiness and test failures.
What's the impact of flaky tests?
Flaky tests can wreck havoc on the development process - from wasted developer time from test retries, to creating bugs and product instability and missed releases, time-consuming flaky tests can grind your development process to a halt.
What is the best way to resolve or fix flaky tests?
Devops, software engineering, and software development teams will often need to compare code changes, logs, and other context across test environments from before the test instability started, and after - adding retries or reruns can also help with debugging. Test detection and test execution tooling can help automate this process as well.
BuildPulse enables you to find, assess impact metrics, quarantine, and fix flaky tests.
What are some strategies for preventing flaky tests?
Paying attention and prioritizing flaky tests as they come up can be a good way to prevent them from becoming an issue. This is where a testing culture is important - if a flaky test case is spotted by an engineer, it should be logged right away. This, however, takes a certain level of hygiene - BuildPulse can provide monitoring so flaky tests are caught right away.
What type of tests have flaky tests?
Flaky tests can be seen across the testing process - unit tests, integration tests, end-to-end tests, UI tests, acceptance tests.
What if I don't have that many flaky tests?
Flaky tests can be stealthy - often ignored by engineers and test runs are retried, they build up until they can’t be ignored anymore. These automated tests slow down developer productivity, impact functionality, and reduce confidence in test results and test suites. Better to get ahead while it’s easy and invest in test management.
It’s also important to prevent regressions to catch flakiness early while it’s manageable.
What languages and continuous integration providers does BuildPulse work with?
BuildPulse integrates with all continuous integration providers (including GitHub Actions, BitBucket Pipelines, and more), test frameworks, and workflows.
Combat non-determinism, drive test confidence, and provide the best experience you can to your developers!
How long does implementation/integration with BuildPulse take?
Implementation/integration takes 5 minutes!