WIP: feat(amazonq): AI code gen % for Q features

[leigaol]

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)

Description

With the release of many Q features(Inline Suggestion, chat, inline chat, /dev, /test, /doc, /review, /transform), we need to know the % code written by all Q features. This requires calculating and reporting the user written code. The reporting of the code contribution of each Q features was already implemented.

% Code Written by Q = Code Written by Q / ( Code Written by Q + Code Written by User)

Ref: aws/aws-toolkit-vscode#5991

Calculate and report the user written code for each language by listening to document change events while Q is not making changes to the editor.

We add flags to know whether Q is making temporary changes for suggestion rendering or Q suggestion is accepted, by doing so, the document change events are coming from the user.

We ignore certain document changes when their length of new characters exceeds 50. Previous data driven research has shown that user tend to copy a huge file from one place to another, making the user written code count skyrocketing but that is actually some existing code not written by the user.

We plan to first collect data from IDEs and let it run in the background in shadow mode before we finish the service side aggregation, fix possible bugs and eventually present the AI code written % to the customers.

Checklist

• My code follows the code style of this project
• I have added tests to cover my changes
• A short description of the change has been added to the CHANGELOG if the change is customer-facing in the IDE.
• I have added metrics for my changes (if required)

License

I confirm that my contribution is made under the terms of the Apache 2.0 license.