Issue Replication Graph Matching for Consistent Defect Reproduction
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Solution Overview
Problem
Conventional technologies for replicating and resolving defect issues in information processing systems rely on manual processes, leading to delays, inconsistency, and the omission of critical information, as different experts provide varying replication suggestions without a defined template, thus prolonging the resolution process.
Innovation Solution
An issue replication system that assigns metrics to resolution requests, stores them in a graph-like data structure, calculates similarity scores with historical cases, and selects relevant historical cases for replication, using semantic matching to identify key steps and parameters for efficient replication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to replicate defect issues, then experts can provide replication suggestions, but the process leads to delays, inconsistency, and omission of critical information
Solution Approach 1:
The system creates a standardized template structure that copies and replicates successful issue reproduction patterns from historical data. The template includes predefined sections for preconditions, steps, parameters, and expected results, allowing consistent replication without manual iteration. This template-based copying approach ensures all critical information is captured uniformly across different replication efforts.
Solution Approach 2:
The system automatically adjusts and optimizes replication parameters by analyzing historical resolution data. It identifies which parameters are critical for replication success and automatically sets appropriate values, reducing manual intervention and ensuring consistent, reliable replication results while accelerating the resolution process.
2Adaptability or versatility
If different experts provide replication suggestions without a defined template, then diverse perspectives are brought to the issue, but inconsistency and omission of critical information occur
Solution Approach 1:
The template structure serves multiple functions simultaneously: it provides a universal framework for all replication efforts, enforces inclusion of critical information through mandatory fields, and maintains flexibility by allowing customization within predefined sections. This multi-functional design ensures consistent information capture while adapting to different issue types.
Solution Approach 2:
The replication template is segmented into distinct sections (preconditions, steps, parameters, expected results), each serving a specific purpose. This segmentation ensures that all critical information categories are systematically captured while maintaining flexibility within each section. The structured breakdown prevents information omission while allowing expert customization where appropriate.
3Productivity
If manual iteration is used to replicate issues, then experts can identify replication steps, but the process is time-consuming and lacks efficiency
Solution Approach 1:
The system performs preliminary analysis of historical data to pre-identify replication steps, parameters, and potential issues before the actual replication begins. By preparing the template structure and pre-filling common fields based on historical patterns, the system eliminates time-consuming manual iteration and accelerates the replication process while maintaining accuracy.
Data Source
AI summary
Methods, system, and non-transitory processor-readable storage medium for an issue replication system are provided herein. An example method includes receiving, by an issue replication system, a resolution request from an issue resolution system, where the resolution request is associated with an issue occurring on an information system. The issue replication system assigns at least one metric to the resolution request, and stores the metric in a graph-like data structure. The issue replication system calculates a similarity score between the graph-like data structure and each of a plurality of graph-like data structures from a resolution repository.