telemetry(amazonq): AI code gen % for Q features #5215
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leigaol
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...c/software/aws/toolkits/jetbrains/services/codewhisperer/telemetry/UserWrittenCodeTracker.kt
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leigaol
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andrewyuq
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rli
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justinmk3
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Jan 16, 2025
…5991 ## Problem 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. ## Solution 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. Note: The JB PR aws/aws-toolkit-jetbrains#5215. The JB implementation depends on a reliable JB internal message bus to pass information. Using VSC event listener might mess up the boolean state of Q editing or not.
karanA-aws
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Jan 17, 2025
…ws#5991 ## Problem 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. ## Solution 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. Note: The JB PR aws/aws-toolkit-jetbrains#5215. The JB implementation depends on a reliable JB internal message bus to pass information. Using VSC event listener might mess up the boolean state of Q editing or not.
leigaol
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...are/aws/toolkits/jetbrains/services/codemodernizer/controller/CodeTransformChatController.kt
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.../toolkits/jetbrains/services/codewhisperer/codescan/utils/CodeWhispererCodeScanIssueUtils.kt
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.../software/aws/toolkits/jetbrains/services/codewhisperer/editor/CodeWhispererEditorManager.kt
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kevluu-aws
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Jan 23, 2025
…ws#5991 ## Problem 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. ## Solution 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. Note: The JB PR aws/aws-toolkit-jetbrains#5215. The JB implementation depends on a reliable JB internal message bus to pass information. Using VSC event listener might mess up the boolean state of Q editing or not.
chungjac
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Jan 24, 2025
…ws#5991 ## Problem 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. ## Solution 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. Note: The JB PR aws/aws-toolkit-jetbrains#5215. The JB implementation depends on a reliable JB internal message bus to pass information. Using VSC event listener might mess up the boolean state of Q editing or not.
s7ab059789
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Feb 19, 2025
…ws#5991 ## Problem 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. ## Solution 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. Note: The JB PR aws/aws-toolkit-jetbrains#5215. The JB implementation depends on a reliable JB internal message bus to pass information. Using VSC event listener might mess up the boolean state of Q editing or not.
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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.
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License
I confirm that my contribution is made under the terms of the Apache 2.0 license.