Efficient pull requests: the pathway to enhanced software quality
Engineering Metrics
Oct 13, 2023
In software engineering, the pull request stands as a beacon of collaboration and quality assurance. For those already immersed in this practice, the challenge often isn't about understanding its significance but about refining the process to achieve maximum efficiency and effectiveness. This article delves deep into the best practices for pull requests, emphasizing the importance of a meticulous yet swift code review process, cycle time, and the depth of reviews.
The Pull Request Paradigm
A pull request, in its essence, is a proposal to merge changes from one branch of code to another. It's a collaborative platform where team members can discuss, review, and ensure that only top-tier code integrates into the main codebase. But how can one ensure that this process is as efficient as it is effective?
The Role of Metrics in Pull Requests
Metrics are the compass guiding the pull request journey. By closely monitoring and understanding these metrics, an engineering team can pinpoint bottlenecks, measure productivity, and ensure the development process aligns seamlessly with the overarching business goals. These metrics include coding time (time spent coding), pickup time (time between pull request is created and first review), review time (time between first review and merge), review depth (comments per pull request), and pull request size (lines of code added and deleted).
These metrics can help you determine which part of the SDLC is the bottleneck, as well as control quality of contributes made - smaller, more reviewable pull requests are preferable to larger ones.
Engineering Team: The Torchbearers of Quality
An optimized pull request process demands active participation and commitment from the entire engineering team. Team members, from developers to engineering leaders, should be well-versed with the significance of metrics and their correlation with the broader software development landscape. Setting benchmarks, ensuring code quality, and implementing best practices are tasks that fall squarely on the shoulders of the engineering organization.
Stakeholders: The Silent Influencers
Beyond the engineering team, stakeholders, encompassing product managers, designers, and sometimes even end-users, play a pivotal role. Their feedback can be instrumental in helping prioritize new features, gauge customer satisfaction, and ensure the development process is in harmony with business goals.
The Art of Code Review
A timely and thorough code review is the linchpin of an efficient development workflow. Delays or superficial reviews can lead to downtime, adversely affecting team performance and overall engineering productivity. Utilizing dashboards and other visualization tools, engineering managers can keep a close eye on the lead time for pull requests, ensuring reviews are both prompt and profound.
Code Quality: The Non-Negotiable
Speed, while essential, should never overshadow quality. Implementing rigorous code review guidelines, leveraging automated testing frameworks, and consistently reviewing engineering metrics like review depth can help maintain an impeccable standard of code quality. Regular assessments of lines of code, pull requests, and other KPIs can set a baseline for continuous improvement.
The Continuous Quest for Betterment
In the ever-evolving realm of software engineering, there's always scope for refinement. Whether it's streamlining the engineering process, embracing new automation tools, or enhancing team communication, the spirit of continuous improvement should be the guiding light for every engineering organization.
BuildPulse: Your Partner in Efficiency
For teams aiming to elevate their pull request process, BuildPulse Engineering Metrics emerges as a game-changer. Beyond offering insights into stale pull requests and reviews, its developer copilot feature automates mundane tasks, ensuring your team remains laser-focused on what truly matters.
In conclusion, efficient pull requests are not just a best practice; they're the bedrock of top-notch software engineering. By emphasizing metrics, fostering a culture of collaboration, and continuously striving for excellence, teams can ensure their pull request process is both streamlined and robust.
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!