dosubot[bot] commented on issue #7765:
URL: 
https://github.com/apache/incubator-devlake/issues/7765#issuecomment-2238566778

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   The Code Quality / Testing data model in Apache DevLake includes a 
comprehensive set of metrics and data points designed to provide detailed 
insights into code quality and testing. Here are the specific metrics included:
   
   - `ProjectKey`
   - `FileName`
   - `FilePath`
   - `FileLanguage`
   - `CodeSmells`
   - `SqaleIndex`
   - `SqaleRating`
   - `Bugs`
   - `ReliabilityRating`
   - `Vulnerabilities`
   - `SecurityRating`
   - `SecurityHotspots`
   - `SecurityHotspotsReviewed`
   - `SecurityReviewRating`
   - `Ncloc`
   - `UncoveredLines`
   - `Coverage`
   - `LinesToCover`
   - `DuplicatedLinesDensity`
   - `DuplicatedBlocks`
   - `DuplicatedFiles`
   - `DuplicatedLines`
   - `EffortToReachMaintainabilityRatingA`
   - `Complexity`
   - `CognitiveComplexity`
   - `NumOfLines`
   
   These metrics are defined in the `CqFileMetrics` struct in the 
`cq_file_metrics.go` file 
<sup>[[1]](https://github.com/apache/incubator-devlake/blob/main/backend/core/models/domainlayer/codequality/cq_file_metrics.go)</sup>.
   
   ### Ingesting Data
   
   Apache DevLake ingests data related to code quality and testing from various 
DevOps tools through its integrative DevOps data plugins. These plugins include 
tools like GitHub, GitLab, JIRA, Jenkins, and others. The data collection, 
expansion, and conversion to the domain layer are managed by running subtasks 
within these plugins. Users can trigger these tasks via a configuration UI or 
API 
<sup>[[2]](https://github.com/apache/incubator-devlake-website/blob/main/blog/2022-06-17-How
 DevLake is up and running/index.md)</sup>.
   
   ### Vision and Idea
   
   The vision behind the Code Quality / Testing data model is to provide a 
unified platform that can ingest, analyze, and visualize fragmented data from 
various DevOps tools. This allows teams to extract actionable insights for 
engineering excellence, developer experience, and community growth. By 
correlating code quality metrics with other data points like build times, teams 
can optimize their development processes, fine-tune CI pipelines, and balance 
the number of tests versus build times.
   
   ### Webhooks for Custom Data
   
   Regarding the use of webhooks for custom or special use case data, Apache 
DevLake currently supports pushing data such as "incidents" and "deployments" 
required by DORA metrics. While the webhook system is quite flexible, it is 
currently limited to specific data types. However, the system can be configured 
to relate incidents with deployments at different levels (e.g., service level, 
product level) 
<sup>[[3]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.21/Plugins/webhook.md)</sup>.
   
   ### Correlating Metrics
   
   Apache DevLake correlates different types of data by using a standard data 
model to bring together data from across the Software Development Life Cycle 
(SDLC). This unified data integration allows for the creation of intuitive, 
use-case driven dashboards that provide actionable insights. For example, you 
can measure code coverage and quality metrics alongside build times to identify 
areas for optimization in your CI pipelines 
<sup>[[4]](https://github.com/apache/incubator-devlake-website/blob/main/docs/Overview/Introduction.md)</sup>.
   
   If you have any more specific questions or need further details, feel free 
to ask!
   
   
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