Issue Tracking Request Type Mapping Module
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Solution Overview
Problem
In complex issue tracking systems with numerous categories and sub-categories, users face challenges in efficiently categorizing issues, especially when unfamiliar with the available categories, leading to time consumption and potential inaccuracies in data quality.
Innovation Solution
The implementation of a request type mapping module that allows users to search and select appropriate request types and service desks based on user queries, utilizing keyword matching with historic issue data to provide a list of recommended options, facilitating efficient categorization and improving data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually categorize issues in complex issue tracking systems with numerous categories and sub-categories, then data quality can be maintained through user expertise, but time consumption increases and accuracy decreases for unfamiliar users
Solution Approach 1:
The system performs automatic categorization without requiring user intervention. The issue tracking system analyzes issue descriptions, titles, and metadata to automatically assign appropriate categories and sub-categories, eliminating the manual categorization step while maintaining or improving accuracy through consistent application of categorization rules and machine learning models.
Solution Approach 2:
The system changes the categorization approach from manual user selection to automated analysis based on multiple parameters including issue title, description, priority level, assigned team, and historical categorization patterns. This multi-parameter analysis enables accurate automatic categorization that adapts to the complexity of the category structure.
2Reliability
If issue tracking systems provide comprehensive category options for all issue types, then data quality and completeness are improved, but user difficulty in locating appropriate categories increases
Solution Approach 1:
The system performs preliminary analysis of the issue data and pre-determines the most appropriate categories before the user needs to make a selection. By analyzing issue metadata, titles, and descriptions in advance, the system prepares and presents a limited set of highly relevant category options, eliminating the need for users to search through comprehensive category lists while ensuring data quality through accurate pre-categorization.
Solution Approach 2:
The system introduces an intelligent intermediary layer between the user and the comprehensive category structure. This intermediary automatically analyzes issues and translates complex category hierarchies into simplified, context-relevant suggestions, allowing users to interact with a manageable subset of categories while the system maintains access to the full comprehensive structure for accurate classification.
3Reliability
If multiple issue tracking systems are deployed for different issue types, then specialization and data quality are improved, but user complexity in selecting the correct system increases
Solution Approach 1:
The issue tracking system is designed with multi-functionality to handle diverse issue types within a single unified platform. Rather than requiring separate specialized systems, the universal system provides adaptable categorization, routing, and tracking capabilities that can accommodate various issue types through configurable categories, workflows, and metadata schemas, eliminating the need for users to select between multiple systems while maintaining specialization through flexible configuration.
Data Source
AI summary
A method for use in an issue creation process, before an issue is submitted, in an issue tracking system (ITS), comprises receiving an unstructured user input expressing an issue description and comprising one or more query keywords; querying a database of historic issue data to determine historic issues from the historic issue data that are relevant to the one or more query keywords and, in response to the determination, extracting request types from the historic issues; generating, from the extracted request types, a list of possible request types for allocation to the unstructured user input; receiving a user input of a particular request type selected from the list of possible request types and, in response, allocating the particular request type to the issue. The issue is submitted to the ITS for routing the issue to an appropriate service desk according to the particular request type.