gistfile1.txt

Objective: Using MCP tools as appropriate I want you to analyze active issues in dotnet/runtime repo to perform a detailed analysis, categorization, scoring, frustration analysis, and generate a roadmap. The categorization should be based on keyword matching, semantic understanding of the issue description, and comment analysis. The content of the issue bodies and comments should not be included in the output, aside from the metadata used for categorization.

Final Engagement Score Formula
Use the following formula to calculate the Engagement Score for each issue:

[
S = \frac{w_c \cdot C^{1.5} \cdot C_{penalty} \cdot w_d \cdot (D + 0.1) \cdot (1 + w_l \cdot A) \cdot (1 + w_u \cdot U)}{1 + w_a \cdot \sqrt{A + \epsilon}}
]

Where:

(C): Total number of comments.
(C_{penalty}):
(C_{penalty} = 0.25) if (C < 10),
(C_{penalty} = 1.0) otherwise.
(D): Distribution factor, representing how evenly comments are spread over the issue's lifespan.
(A): Age of the issue in years ((A = \text{(LastUpdated - CreatedAt)})).
(\epsilon = 0.1): Small constant added to prevent division by zero or undervaluing very recent issues.
(U): Total number of upvotes (if available, otherwise treated as 0).
Weights:
(w_c = 2): Emphasizes the number of comments.
(w_d = 1): Balances the importance of distribution.
(w_l = 0.5): Boosts older issues for longevity.
(w_u = 0.5): Boosts issues with upvotes to reflect their priority/interest.
(w_a = 1): Penalizes older issues for staleness.
Handling Missing Upvotes:
If the Upvotes Field is Missing: Treat Upvotes as 0, meaning the Engagement Score will be calculated without any boost from upvotes.
If Upvotes Exist: The Upvotes field will boost the engagement score by contributing to the overall calculation of the score.
Frustration Score Formula
To quantify the frustration users are experiencing with various issues, we can create a Frustration Score based on several factors such as:

[
F = \left( w_c \cdot C \right) + \left( w_s \cdot S \right) + \left( w_r \cdot R \right) + \left( w_t \cdot T \right)
]

Where:

(C) = Number of comments on the issue.
(S) = Sentiment score, based on the proportion of negative sentiments (e.g., using keywords like "bug", "error", "frustrating").
(R) = Recurrence factor, a score indicating how often users bring up the issue.
(T) = Time factor, where older issues get higher scores.
Step-by-Step Process:
Number of Comments: Directly taken from the dataset.
Sentiment Analysis: We analyze the comments for negative keywords related to frustration.
Recurrence: Based on how many times the issue has been referenced in other issues or re-opened over time.
Time Opened: Calculate the age of the issue and increase the frustration score if the issue has been unresolved for a long time.
Initial Weights Proposal:
(w_c = 1): The number of comments is a significant indicator of frustration.
(w_s = 2): Negative sentiment typically correlates strongly with frustration.
(w_r = 1.5): Recurrence is important but less impactful than sentiment or comments.
(w_t = 0.5): Older issues cause more frustration, but not as much as direct sentiment or comment count.
Table Output (CSV):
The output dataset will now include Upvotes as part of the analysis, along with Engagement Score and Frustration Score. The columns in the CSV should be as follows:

Title	URL	Comment Count	Unique Commenters	Upvotes	Engagement Score	Years Opened	Frustration Score
Consider requiring Antiforgery Validation by default	https://github.com/dotnet/runtime/issues/4813	29	22	1	30.91	3.2	65.4
We need a solution for API Explorer + Custom ModelBinder	https://github.com/dotnet/runtime/issues/4825	25	18	19	45.30	2.5	72.1
Improve validation for non-interactive consumption	https://github.com/dotnet/runtime/issues/4840	30	12	0	35.72	3.0	50.3
Add a way to access reason of a Mvc filter failure	https://github.com/dotnet/runtime/issues/4847	10	5	0	20.10	4.0	40.0
Ability to translate all DataAnnotations without ErrorMessage	https://github.com/dotnet/runtime/issues/4848	15	8	44	60.72	3.5	80.2
Outcome:
Upvotes are included in the calculation of the Engagement Score if available, and treated as 0 if missing. This ensures the formula works for both new and older datasets.
The Frustration Score and other metrics remain unchanged, providing a comprehensive view of issue engagement and user frustration.
The updated table will reflect Engagement Score and Frustration Score based on these new variables, ensuring accurate insights across datasets.
The above are general instructions. In this case, please perform the analysis using issues in dotnet/runtime that are active and have the label "area-System.IO" only. If you have any limitations in your analysis that are relevant, share them.