Issue Tracking System Semantic Segmentation for Compound Requests
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Solution Overview
Problem
Conventional issue tracking systems are inefficient due to the time and resource-consuming processes of adding, editing, and updating issues, leading to users submitting abstract, compound issue requests that lack granularity and specificity, making them difficult to accurately track.
Innovation Solution
An issue tracking system that includes a client device and a host service with a processor capable of determining a divisibility score for issue requests based on semantic content, generating multiple issue request templates, and suggesting subdivision of compound requests into more discrete issues, utilizing lemmatization, similarity analysis, and predictive modeling to improve granularity and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users submit abstract, compound issue requests to minimize interaction time, then user interaction efficiency is improved, but measurement precision and tracking accuracy deteriorate
Solution Approach 1:
The system automatically segments compound issue requests into multiple discrete issue requests by analyzing semantic content and identifying distinct tasks or problems within the compound request. This segmentation maintains tracking accuracy while minimizing user interaction, as the system performs the subdivision automatically based on natural language processing of the issue description.
2Measurement precision
If users submit detailed, discrete issue requests to improve tracking accuracy, then measurement precision is improved, but time and resources required for adding and updating issues increase
Solution Approach 1:
The system performs preliminary analysis of issue requests to automatically identify when segmentation is needed and prepares the subdivided issues in advance. By analyzing the semantic content and structure of the issue request before final submission, the system pre-processes the information to enable automatic segmentation, thereby maintaining tracking accuracy without requiring users to manually create detailed discrete issues.
3Measurement precision
If the system automatically subdivides issue requests into multiple discrete issues, then measurement precision and tracking accuracy are improved, but device complexity increases
Solution Approach 1:
The system employs self-service mechanisms through automated natural language processing and semantic analysis to identify and segment compound issues. The system analyzes the issue description, identifies key entities and relationships, and automatically subdivides the request without requiring complex manual configuration or intervention, thereby maintaining tracking accuracy while managing system complexity through autonomous operation.
Data Source
AI summary
An issue tracking system configured to determine whether an issue request submitted by a user of the issue tracking system can, or should, be subdivided into two or more issue requests. In some implementations, the issue tracking system is configured to extract a content item of the issue request (e.g., title, description, and the like) in order to perform a semantic and/or syntactic analysis of that content item. Upon determining that the content item includes two or more clauses linked by a coordinating, subordinating, or correlative conjunction, the system can provide a recommendation to the user to submit discrete two or more issue requests, each one of which corresponds to a single linked clause of the content item.