Cross-Project Issue Tracking with Similarity-Based Duplicate Creation
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Solution Overview
Problem
Traditional issue tracking systems require manual entry of each issue, which is time and resource-consuming, and do not efficiently suggest potential issues based on similarity across projects.
Innovation Solution
An issue tracking system that determines a similarity score between issue requests across different projects, identifies similar issues using a similarity threshold, and suggests supplemental issue requests based on seed issue records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual entry of each issue is required, then issue tracking accuracy is maintained, but time consumption and resource usage increase significantly
Solution Approach 1:
The system creates duplicate issue requests by copying existing issue data from seed issues. When a new issue is submitted, the system identifies similar seed issues and automatically generates duplicate issue requests with pre-filled content, eliminating manual re-entry while maintaining consistency across projects.
Solution Approach 2:
The system performs preliminary analysis by calculating similarity scores between new issues and existing seed issues before issue creation. This preliminary action identifies which issues should be duplicated and prepares the duplicate issue templates in advance, reducing the time required for issue entry and review.
2Measurement precision
If manual entry of each issue is required, then issue data accuracy is maintained, but resource consumption increases
Solution Approach 1:
The system creates duplicate issue requests by copying existing issue data from seed issues. When a new issue is submitted, the system identifies similar seed issues and automatically generates duplicate issue requests with pre-filled content, eliminating manual re-entry while maintaining consistency across projects.
3Productivity
If similarity-based suggestion system is implemented, then issue reporting efficiency is improved, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical processes of issue entry and similarity comparison with automated computational operations. The host service automatically calculates similarity scores, identifies seed issues, and generates duplicate issue requests without human intervention, reducing system complexity from the user perspective while improving efficiency.
4Measurement precision
If similarity threshold filtering is applied, then relevant issue suggestions are provided, but processing time increases
Solution Approach 1:
The system applies similarity threshold filtering to identify seed issues that meet the relevance criterion. By setting a threshold (e.g., 70% similarity), the system processes only sufficiently similar issues rather than all possible comparisons, balancing suggestion relevance with acceptable processing time.
Data Source
AI summary
An issue tracking system for tracking software development tasks is described herein. The issue tracking system may be configured to receive new issue requests from a client device and associate the new issue requests with one or more clusters of previously stored issue records. The issue tracking system may also determine similarity between issues in a first cluster of stored issue records and issues in a second cluster that is associated with a different software development project. Based on a determination that the issue similarity exceeds a threshold, the user may be prompted with one or more recommendations for a subsequent issue request or issue request content.