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Integrate a fuzzy search algorithm into the GitHub Action to detect and prevent the creation of duplicate issues based on similarities in their titles and descriptions.
Details
Algorithm Choice: Utilize the Levenshtein distance or Jaccard Index to measure the similarity between the title and description of the new issue and existing issues in the repository.
Threshold Setting: Define a similarity threshold to determine when an issue is considered a duplicate. Issues surpassing this threshold will not be created, and a reference to the existing similar issue will be provided instead.
Implementation Considerations:
Ensure that the search algorithm does not significantly impact the execution time of the action.
Handle edge cases where the issue descriptions or titles are exceptionally long or contain special formatting.
Quality Assurance: Include tests to validate the accuracy and efficiency of the fuzzy search implementation. Ensure that the feature does not interfere with the existing functionalities of the GitHub Action.
Benefits
Reduced Redundancy: Minimize duplicate issues, making the issue tracker easier to manage and navigate.
Enhanced Efficiency: Save time for developers and maintainers by automatically preventing duplicate issue submissions.
Improved Issue Management: Streamline issue management processes by automatically identifying and referencing similar existing issues.
The text was updated successfully, but these errors were encountered:
Objective
Integrate a fuzzy search algorithm into the GitHub Action to detect and prevent the creation of duplicate issues based on similarities in their titles and descriptions.
Details
Benefits
The text was updated successfully, but these errors were encountered: