DORA metrics, productivity, and team performance

How flaky tests corrupt change failure rate (and how to fix it)

Flaky tests push CFR in both directions: phantom rollbacks inflate it, rerun-to-green deflates it. Here's how to clean the signal before your next exec review.

Deploy frequency: the DORA metric everyone measures wrong

Deploy frequency is the easiest DORA metric to game and the hardest to interpret. Here's how to measure it honestly and what it's really telling you.

gotestsum: making go test output actually useful in CI

gotestsum turns go test's wall of text into structured, CI-friendly output. Here's how to set it up well, and where its rerun feature quietly hides flaky tests.

Query your CI health from a script: a read API for flaky tests, coverage, and runs

How to pull flaky-test history, coverage, and recent runs from a read API so you can build your own dashboards, Slack alerts, and release gates.

Cycle time: the DORA metric that actually changes behavior

Cycle time is the DORA-adjacent metric leaders love and misread constantly. Here's how to measure it, what it hides, and how teams game it.

Change failure rate: the DORA metric everyone quotes and nobody measures right

Change failure rate is the DORA metric leaders love to cite and teams love to game. Here's how to measure it honestly — and why flaky tests corrupt it.

Change failure rate: the DORA metric everyone measures wrong

Of the four DORA metrics, change failure rate is the one teams most often measure wrong — and the one most contaminated by flaky CI. Here's how to get it right.

Change failure rate is lying to you if your tests are flaky

If flaky tests trigger rollbacks or reruns, your change failure rate is counting noise as incidents. Here's how to fix the signal before it reaches leadership.

Git metrics that actually tell you something useful

Most teams collecting git metrics end up measuring the wrong things. Here's how to cut through the noise and focus on signals that actually reflect engineering health.

Building culture with cycle times

Engineering Metrics: How cycle time can be a cornerstone in building a robust engineering culture.

Code Readability

Engineering Metrics: How does it impact the broader development process?

Quality gates in pull requests

Engineering Metrics: Quality gates play a pivotal role in ensuring that the code meets the desired standards before it's merged.

Different ways of measuring code complexity

Engineering Metrics: Add a sentence here that will hook readers

Which git metrics should I be looking at?

Engineering Metrics: With a plethora of metrics available, which ones truly matter?

What is tech debt?

Engineering Metrics: Imagine borrowing time from the future to meet present needs.

How engineering leaders maintain sync with stakeholders

Engineering Metrics: This bridge is built on trust, transparency, and most importantly, metrics.

Preventing performance review bias

Engineering Metrics: Unconscious preferences can skew perceptions, biases can lead to unfair and unrepresentative assessments.

Why does deploy frequency matter?

Engineering Metrics: Let's explore the significance of deploy frequency and its implications in the software development landscape.

Efficient pull requests: the pathway to enhanced software quality

Engineering Metrics: We dive into best practices for pull requests, emphasizing the importance of a meticulous yet swift code review process.

Benefits of engineering intelligence

Engineering Metrics: The phrase "what gets measured gets managed" holds profound significance.

Building a data-driven engineering culture

Engineering Metrics: Let's explore the steps and strategies that can help teams harness the power of metrics to drive excellence.

Beyond story points and ticket status: what to measure

Points measure what the team predicted and ticket status measures what someone remembered to update. Here is what to measure instead, with examples.

The power of sprint velocity

Engineering Metrics: What is sprint velocity, and how does it fit into a broader, data-driven engineering culture?

Measuring your software delivery pipeline effectively

Engineering Metrics: Sofware delivery pipelines encompass both the 'how' - the stages like coding time, pickup time, and review time, and the 'what' - outcomes.