Improve productivity with just these 5 metrics
Engineering Metrics
Oct 9, 2023
In software engineering, productivity is the linchpin that determines the success of any project. While there are numerous metrics that engineering teams can track, focusing on the right ones can make all the difference. Let's delve into five key metrics that can significantly enhance productivity and why tools like BuildPulse's Impact are revolutionizing the way these metrics are approached.
Coding Time
The time taken to write code is a fundamental metric that reflects the efficiency of the development process. By monitoring coding time, engineering leaders can identify if team members are spending too much time on specific tasks, indicating potential bottlenecks or areas that require optimization. Moreover, it provides insights into the complexity of tasks and can guide allocation decisions.
Pickup Time
Once a task is assigned, the time taken for a developer to start working on it is the pickup time. Delays in pickup time can indicate challenges in the workflow, lack of clarity in task definitions, or resource allocation issues. By optimizing pickup time, engineering teams can ensure that tasks move seamlessly through the development process.
Review Time
Code review is an integral part of the software development process, ensuring code quality and alignment with engineering projects' objectives. The time taken for this review process, from the moment a pull request is made to when it's approved, is crucial. Extended review times can indicate challenges in the review process, the need for better frameworks, or areas where automation can be implemented.
Deploy Time
Deploy time represents the duration from when code is approved to when it's deployed. Delays in deploy time can be due to bottlenecks in the deployment process, challenges with devops, or issues with automation. Optimizing deploy time ensures that new features are rolled out efficiently, meeting business goals and ensuring customer satisfaction.
Cycle Time
Cycle time is the sum of the above metrics, providing a holistic view of the entire software development process. It encapsulates the time from when a task is initiated to its completion. A shorter cycle time indicates an efficient development process, while a longer one signals potential inefficiencies or bottlenecks.
DORA Metrics vs. Granular Insights
While DORA metrics (Deployment Frequency, Lead Time for Changes, Time to Restore Service, and Change Failure Rate) provide a broad overview of an engineering organization's health, they often don't offer the granular insights needed to pinpoint specific challenges. For instance, while DORA metrics might indicate a longer lead time, they won't necessarily reveal if the delay is due to extended code review processes or challenges in the workflow of specific development teams.
The Power of BuildPulse's Impact
To truly harness the power of these five metrics and optimize engineering productivity, teams need tools that provide deeper insights. BuildPulse Engineering Metrics is one such tool that dives deeper than traditional metrics. It offers insights into code velocity and quality metrics at the repo, team, or individual level.
With features that notify developers about estimated PR review times, stale pull requests, and stale reviews, BuildPulse ensures that the team remains agile and efficient. Such real-time notifications can significantly reduce delays in the review process, ensuring that the cycle time remains optimal.
Conclusion
In the intricate world of software engineering, focusing on the right metrics can be the difference between a project that's delivered on time with high code quality and one that faces delays and challenges. By concentrating on these five key metrics and leveraging tools like BuildPulse Engineering Metrics, engineering teams can navigate the challenges of software development with finesse.
Incorporating tools that provide real-time insights, like BuildPulse, can be the game-changer that engineering teams need to optimize their processes, enhance productivity, and consistently deliver high-quality software.
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!