Can someone break down how SonarQube calculates technical debt and code smell metrics so I can explain it simply to my engineering team tomorrow?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Can someone break down how SonarQube calculates technical debt and code smell metrics so I can explain it simply to my engineering team tomorrow?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
site:docs.sonarsource.com/sonarqube-server technical debt remediation effort code smells maintainability rating debt ratio formula Google ↗
site:docs.sonarsource.com/sonarqube-server "Technical Debt" "Code Smells" remediation cost Google ↗
- Understanding measures and metrics | SonarQube Server 2025.4 LTA · docs.sonarsource.com
- Understanding measures and metrics | SonarQube Server · docs.sonarsource.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
site:docs.sonarsource.com/sonarqube-server technical debt remediation effort code smells maintainability rating debt ratio formula OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
site:docs.sonarsource.com/sonarqube-server "Technical Debt" "Code Smells" remediation cost OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |