This repository contains a set of QA-related metric queries, visualized through interactive charts built with Grafana using the TestData DB mock data source.
Note: The data is simulated for demonstration purposes. The visualizations are based on real queries I used in previous QA projects.
As a QA professional, I care not only about validating features, but also about building a clear and objective picture of product quality. This dashboard reflects that approach by tracking:
- Bottlenecks in delivery
- Defects by severity and root cause
- QA process efficiency
Chart type: Bar chart
Metric: Average resolution time for Defects, Stories, and Story Bugs
defect_resolution_time.sql
Why this chart?
It helps identify which issue types take longer to resolve, offering insight into process delays.
Chart type: Pie chart
Metric: Proportion of defects by severity level
defects_by_severity.sql
Why this chart?
Clearly communicates the impact of reported bugs by highlighting the ratio of critical vs. minor issues.
Chart type: Gauge or bar
Metric: Ratio of Story Bugs to total detected issues
qa_efficiency.sql
Why this chart?
Helps evaluate how often issues are caught during the story phase versus later, signaling QA effectiveness.
All visualizations were created using:
grafana-testdata-datasource(mock data)- Field transformations and display name overrides in Grafana
- Hand-picked chart types to best match each metric’s goal
- Decisions based on my QA experience with real-world products (including blockchain and Web3 projects)
I'm happy to walk you through the dashboard in a short Loom video or live call — just let me know!
I'm Gisela Martínez, a QA Engineer passionate about quality, user experience, and continuous improvement.
I’ve worked in backend, mobile, web, and blockchain environments — and I love bringing clarity through well-structured metrics.