Building a data-driven engineering culture

Engineering Metrics

Oct 12, 2023

In the realm of software engineering, data is the compass that guides decision-making, optimizes processes, and fosters a culture of continuous improvement. But how can engineering teams transition to a data-driven culture? Let's explore the steps and strategies that can help teams harness the power of metrics to drive excellence.

The Power of Metrics in Engineering

Metrics are more than just numbers; they're the heartbeat of an engineering organization. They provide insights into team performance, bottlenecks, and areas ripe for optimization:

  • Cycle Time: A pivotal metric, cycle time measures the duration from the inception of a task to its successful deployment. It encompasses coding, reviewing, testing, and merging.

  • Lead Time: This metric captures the timeframe from when a feature is requested to when it's delivered, crucial for stakeholders and decision-making.

  • Open Pull Requests: This metric captures progress on work currently being done.

  • Long-lived Pull Requests: This metric finds bottlenecks in the development cycle and identifies issues early on.

  • Team Key Performance Indicators (KPIs): Not all metrics are created equal. KPIs help engineering teams pinpoint which metrics are most vital. By focusing on these KPIs, teams can channel their efforts towards areas that yield the most significant impact.

Dashboards: The Visual Representation of Metrics

Dashboards offer a consolidated view of various metrics, from pull requests, lines of code, to the intricacies of cycle time. They empower engineering leaders and team members alike with actionable insights, facilitating informed decision-making.

Setting and Adjusting Goals

While setting goals is crucial, it's equally important to remain flexible. As teams gather more data and gain insights, goals might need adjustments. This iterative approach ensures that goals remain aligned with changing contexts and circumstances.

The Role of Engineers in a Data-Driven Culture

A data-driven culture isn't just for leadership; it's for every engineer. Regularly reviewing progress against goals with engineers fosters a sense of ownership. Moreover, involving engineers in conversations about metrics ensures that the data collected is relevant and actionable.

Embracing Continuous Improvement

Continuous improvement is the cornerstone of a data-driven culture. By regularly reviewing metrics, identifying bottlenecks, and implementing solutions, engineering teams can consistently elevate their performance.

The Significance of Code Review

Code reviews are more than just a quality check; they're a treasure trove of data. By analyzing code review metrics, teams can gain insights into code quality, developer productivity, and areas that need attention.

Navigating the Development Process with Data

From the initial stages of software development to the final deployment, data can guide every step. By understanding metrics like lines of code, pull requests, and downtime, teams can streamline their development process.

The Future: Automation and DevOps

As engineering teams evolve, automation and DevOps will play pivotal roles. Automation can reduce manual tasks, while DevOps practices can further optimize the software development lifecycle.

BuildPulse: Elevating Data-Driven Engineering

To truly harness the power of a data-driven engineering culture, teams need cutting-edge tools. BuildPulse Engineering Metrics offers deep insights into engineering productivity metrics, enabling teams to optimize their processes and deliver unparalleled value.


Building a data-driven engineering culture is a journey, not a destination. It requires commitment, collaboration, and a willingness to adapt. By focusing on the right metrics, setting flexible goals, and involving every team member in the process, engineering teams can set themselves up for success. With tools like BuildPulse Engineering Metrics, the journey becomes even more rewarding, driving innovation, growth, and customer satisfaction.


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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