Building a Test Dashboard in Metabase
How to build a test execution dashboard in Metabase: SQL queries, schema design, and pitfalls SDETs and platform enginee...
Read articleLatest Articles
Artifact Expiry Silently Breaks Failure Trends
Artifact expiry policies quietly corrupt historical failure trend data. Here's how to detect the gap, preserve what matt...
Read articleDeveloping Dashboards for CI/CD Pipelines
Build CI/CD test dashboards that surface real signal: SQL queries, Grafana panels, GitHub Actions YAML, and ClickHouse s...
Read articleGitLab CI Test Reports: End to End
GitLab CI test reports: how artifacts, JUnit XML, and the test summary UI wire together — plus SQL analytics, pitfalls, ...
Read articleCircleCI Insights vs Jenkins Test Reporting
CircleCI Insights vs Jenkins test reporting: real trade-offs, SQL queries, and dashboard configs for SDETs who need sign...
Read articleRerun Depth Skews MTTD Across Pipelines
Rerun depth silently inflates MTTD across pipelines. Here's how to measure the distortion and fix your CI reporting befo...
Read articleDatadog CI Visibility vs Homegrown Grafana
Datadog CI Visibility vs a homegrown Grafana setup: real trade-offs, SQL, YAML, and dashboard config for SDETs and platf...
Read articleJUnit XML as a Grafana Data Source
How to parse JUnit XML reports, load them into PostgreSQL or ClickHouse, and build Grafana dashboards that surface real ...
Read articleRerun Gaps in Jenkins: Silent Retry Misconfiguration
Jenkins retry misconfig hides flaky tests behind inflated pass rates. Here's how to find the gaps using rerun data, SQL,...
Read articleBranch Filtering Skews Multi-Pipeline Pass Rates
Branch filtering in GitHub Actions, Jenkins, and multi-pipeline dashboards silently skews pass rate. Here's how to detec...
Read articlePipeline Pass Rate Drift and Broken Stages
Pass rate drift masks broken pipeline stages. Here's how to detect it with SQL, GitHub Actions, and Grafana before it si...
Read articleWhy Multi-Agent LLM Systems Fail in CI
Multi-agent LLM systems fail in CI for predictable reasons: context drift, tool-call loops, and silent degradation.
Read articleTest Result Storage: PostgreSQL, ClickHouse, BigQuery
Optimize your CI/CD test result storage with PostgreSQL, ClickHouse, and BigQuery for deeper engineering insights.
Read articleVisualizing Test Results in GitHub Actions
Learn how to turn GitHub Actions test results into actionable insights with dashboards and analytics, beyond simple pass/fail metrics.
Read articleMulti-Pipeline Visibility for Engineering Leaders
Unlock engineering insights with comprehensive multi-pipeline visibility.
Read articleReal-Time Test Pipelines: From Run to Insight
Transform test results into actionable engineering insights with real-time CI/CD pipelines.
Read articleSlack/Teams Notifications That Actually Help
Transform your CI/CD notifications from noise to actionable insights with real-world tools and techniques.
Read articleBuild a Test Dashboard for CI/CD (Step-by-Step)
Create a CI/CD test dashboard to transform test results into actionable engineering insights.
Read articleAll CI/CD Articles
- Building a Test Dashboard in Metabase
- Artifact Expiry Silently Breaks Failure Trends
- Developing Dashboards for CI/CD Pipelines
- GitLab CI Test Reports: End to End
- CircleCI Insights vs Jenkins Test Reporting
- Rerun Depth Skews MTTD Across Pipelines
- Datadog CI Visibility vs Homegrown Grafana
- JUnit XML as a Grafana Data Source
- Rerun Gaps in Jenkins: Silent Retry Misconfiguration
- Branch Filtering Skews Multi-Pipeline Pass Rates
- Pipeline Pass Rate Drift and Broken Stages
- Why Multi-Agent LLM Systems Fail in CI
- Test Result Storage: PostgreSQL, ClickHouse, BigQuery
- Visualizing Test Results in GitHub Actions
- Multi-Pipeline Visibility for Engineering Leaders
- Real-Time Test Pipelines: From Run to Insight
- Slack/Teams Notifications That Actually Help
- Build a Test Dashboard for CI/CD (Step-by-Step)
- Custom Dashboards: Grafana, Looker, Datadog
- Jenkins Test Reporting That Does Not Suck