Defect Resolution Routing Using Skill Scores and Domain Matching
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Solution Overview
Problem
Conventional defect issue resolution in information processing systems is inefficient due to manual processes that lead to bottlenecks among domain experts, as complex issues often require solutions involving multiple domains and are inefficiently assigned.
Innovation Solution
An issue resolution system that identifies resources with a skill score meeting a threshold, determines a targeted resource with a maximum overall defect issue resolution score, and updates the required domain set by removing unnecessary domains, using Cosine Similarity to match historical defect issues and select the most skilled and available experts for collaborative resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for defect issue resolution, then domain experts can be assigned to issues, but bottlenecks occur among experts and resolution efficiency decreases
Solution Approach 1:
An automated issue resolution system acts as an intermediary between defect issues and domain experts. The system receives defect issues, analyzes them using Cosine Similarity to match with historical issues, identifies required domains, and automatically assigns issues to appropriate experts based on skill scores. This intermediary system eliminates manual assignment bottlenecks and optimizes the matching process between issues and experts.
Solution Approach 2:
The patent replaces manual mechanical processes of issue assignment and expert selection with an automated computational system. The system uses Cosine Similarity calculations, skill score comparisons, and algorithmic decision-making to substitute the manual mechanical processes of human reviewers assigning issues, thereby improving efficiency and reducing delays.
2Reliability
If complex defect issues are assigned to multiple domain experts, then comprehensive resolution is achieved, but the assignment process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary analysis of defect issues by comparing them with historical defect issues using Cosine Similarity before assignment. This preliminary action identifies the required domains and potential experts in advance, so that when complex issues need multiple experts, the system has already prepared the domain set and skill score evaluations, reducing the complexity of the actual assignment process.
Solution Approach 2:
The system dynamically adjusts the required domain set during the assignment process. As experts are selected and assigned, the system updates and refines the domain set, removing domains that have been addressed. This dynamic approach allows the system to handle complex multi-domain issues efficiently by adapting the assignment process rather than requiring all domains to be predetermined and assigned simultaneously.
Data Source
AI summary
Methods, system, and non-transitory processor-readable storage medium for an issue resolution system are provided herein. An example method includes receiving, by an issue resolution system, from a tester system, a defect issue that occurred on the tester system while the tester system was executing. The issue resolution system identifies at least one resource, where one or more resources are associated with a skill score associated with at least one target domain in a required domain set, where the skill score meets a threshold. The issue resolution system identifies a targeted resource having a maximum overall defect issue resolution score, where one or more resource comprises the targeted resource. The issue resolution system updates the required domain set by removing at least one domain where the required domain set comprises the at least one domain.