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fix: added tests and redactPrivateRepoComments #24
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shiv810
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94bd0c4
fix: added tests
sshivaditya 0c7f988
fix: added redactPrivateRepoComments
sshivaditya 16050b0
fix: redactPrivateRepoCommentsadded added
sshivaditya 0a32df2
fix: tests for the latest spec
sshivaditya 8ac7a0b
chore: updated generated configuration
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Original file line number | Diff line number | Diff line change |
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{ | ||
"title": "Bug: NullPointerException in Python API Handler", | ||
"issue_body": "Title: Bug: NullPointerException in Python API Handler\nDescription: A NullPointerException occurs in the get_user function of the Python API handler when a null value is passed for user_id. Implement a null check to handle this scenario.\nSteps to Reproduce: Pass a null user_id to get_user. Observe the exception in the API logs.\nExpected Behavior: Null values should be handled without causing an exception.\nCurrent Behavior: The function throws a NullPointerException.\nAttachments: Stack trace log." | ||
} |
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,4 @@ | ||
{ | ||
"title": "Bug: NullPointerException in Python API Handler", | ||
"issue_body": "Description: The get_user function in the Python API handler throws a NullPointerException when the user_id is not provided. This needs a null check to prevent the exception and handle invalid user IDs.\n\nSteps to reproduce: Call the get_user function with a null user_id. Check the exception in the API logs.\n\nExpected behavior: NullPointerException should be avoided with proper null handling.\n\nCurrent behavior: Exception NullPointerException is thrown.\n\nAttachments: Exception trace log." | ||
} |
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Original file line number | Diff line number | Diff line change |
---|---|---|
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{ | ||
"title": "Review Cache Strategies for Java Service Layer to Handle High Load", | ||
"issue_body": "Description: The Java service layer's performance deteriorates under high load, partly due to the absence of effective caching. Evaluate and implement advanced caching strategies to ensure the service layer performs optimally during peak usage.\nSteps to Reproduce:\nSimulate high load on the Java service layer.\nAnalyze performance metrics and identify bottlenecks.\nExpected Behavior: Service layer maintains stability and performance under high load with improved caching strategies.\nCurrent Behavior: Performance degradation and potential instability under high load.\nAttachments: N/A" | ||
} |
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Original file line number | Diff line number | Diff line change |
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{ | ||
"title": "Feature Request: Add Caching to Java Service Layer", | ||
"issue_body": "Description: The Java service layer does not currently utilize caching, resulting in performance issues with frequent requests. Implement caching to improve performance for repeated queries.\nSteps to Reproduce: Make frequent requests to the Java service layer. Monitor performance and response times.\nExpected Behavior: Improved performance with caching enabled.\nCurrent Behavior: Slower response times due to lack of caching.\nAttachments: N/A" | ||
} | ||
|
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I think it makes sense to test with our real specifications.
Tip
You can look through "closed as not complete" issues that contain comments like "duplicate." We generally link to the correct issue when closing as not complete.
You might need to use GraphQL because GitHub search UI does not clearly show that it is "closed as not complete" example.
I originally located the above using this comment.
Perhaps there is a way to "train" the system that all of our issues that exist open now are unique? We have been quite good about this.
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ubiquity-os/plugins-wishlist#50 (comment)
I believe using this system would be effective. Docs would contain the right context anchors uniformly, then when we fine-tune/train our own model with these docs we train it around these context anchors specifically although they'll be pretty effective without it imo.
If we only have docs for closed tasks then this already effectively treats open tasks as unique and a spec contains the why and typically the how also, which our docs contain for each task, we'll have improved performance in my opinion.
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I believe we can utilize https://openpipe.ai/. Once we have our embeddings and text corpus ready, we can fine-tune an LLM for this use case. I can elaborate further, but I'm not quite clear on what you mean by 'all of our current issues are unique.' There should be some common themes in the tickets and workflow
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Unique means that the plugin should not flag them as redundant.
This is a tangible test with a simple boolean result that we can use to tweak our approach.
If the test says any are a match, then keep adjusting.
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We could tokenize the issues and use the Dice coefficient for comparison. If the coefficient falls below a certain threshold, we can permit the creation of that issue; otherwise, we won't.