Benefits of engineering intelligence

Engineering Metrics

Oct 13, 2023

In the dynamic realm of software engineering, the phrase "what gets measured gets managed" holds profound significance. To optimize and improve any process, one must first measure its components. This is where the power of an engineering analytics platform comes into play. Such platforms provide engineering leaders with the metrics and insights they need to identify bottlenecks, streamline workflows, and foster a culture of best practices within their teams.

The Imperative of Metrics

Metrics serve as the backbone of any analytics platform. They provide quantifiable data that can be analyzed to gain insights into the engineering process. For an engineering team, these metrics offer a clear picture of their performance, helping them identify areas of improvement and excellence.

Cycle Time: The Pulse of Productivity

One of the most crucial engineering metrics is cycle time. It measures the duration from the initiation of a task to its completion. By breaking down cycle time into its components - coding time, pickup time, review time, and deploy time - teams can pinpoint where delays are occurring. This granular view allows for targeted optimization, ensuring that projects are completed on time and stakeholders are satisfied.

Code Velocity: Beyond Just Lines of Code

While lines of code can provide a basic understanding of developer productivity, code velocity offers a more comprehensive view. It encompasses various aspects such as coding time, the speed of pull requests, and the efficiency of the code review process. By monitoring code velocity, engineering managers can ensure that their teams are not just working hard but also working smart.

Throughput: The Flow of Work

Another vital metric is throughput, which focuses on the number of pull requests opened and closed. A high throughput indicates a team that's efficiently managing its workload, while a low throughput can signal bottlenecks or challenges in the development process.

Quality Metrics: The Depth of the Code

Beyond speed and efficiency, the quality of the code is paramount. Metrics like PR size, code added, and code deleted provide insights into the intricacies of the codebase. A balanced ratio of code added to code deleted can indicate a healthy codebase, while deviations can signal potential issues.

Building a Culture of Best Practices

With the insights provided by an engineering analytics platform, teams can foster a culture of best practices. By continuously measuring and optimizing, teams can ensure that their workflows are efficient, their code is of high quality, and their projects align with business goals.

The Power of Dashboards

Dashboards, a feature of many analytics platforms, provide a visual representation of metrics. They allow engineering leaders to quickly gauge the performance of their teams, identify trends, and make informed decisions. In the fast-paced world of software development, having real-time insights at one's fingertips can be a game-changer.

The Role of Engineering Leaders

Engineering leaders play a pivotal role in leveraging the power of analytics platforms. By championing the importance of metrics, they can ensure that their teams are always striving for excellence. Moreover, by using these insights to guide decision-making, they can drive continuous improvement within their engineering organization.

BuildPulse: Your Partner in Engineering Excellence

For teams looking to harness the power of engineering analytics, BuildPulse Engineering Metrics emerges as an invaluable tool. Not only does it provide comprehensive metrics, but its developer copilot feature also notifies teams about stale pull requests and automates routine tasks. By ensuring that reviews are timely and efficient, BuildPulse helps teams maintain impeccable code quality and streamline their workflows.

In conclusion, an engineering analytics platform is not just a tool; it's a catalyst for excellence. By providing teams with the metrics and insights they need, such platforms empower them to optimize their processes, enhance their code quality, and achieve their software engineering goals.


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!

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