Context-Aware Software Issue Prioritization via Automated Data Augmentation
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Solution Overview
Problem
Existing software issue tracking systems often assign priority ratings based on limited or subjective information, failing to adequately account for context that could significantly impact the urgency or importance of issues, leading to suboptimal prioritization and resource allocation.
Innovation Solution
The system identifies and utilizes context data to generate a context-aware priority rating by accessing issue metadata and retrieving relevant context data from repositories, detecting dependencies, and estimating the potential impact of issues, thereby providing a more objective and comprehensive prioritization of software-related issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If priority rating is assigned based on limited or subjective information, then the prioritization process is simple and quick, but the accuracy and objectivity of priority ratings deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically gathering context data from multiple sources (issue descriptions, software component information, user feedback, historical data) before priority rating is assigned. This pre-collection of relevant information enables more accurate priority ratings without requiring complex manual analysis at the time of prioritization.
Solution Approach 2:
The patent introduces an intermediary prioritization system that acts as a mediator between raw issue data and final priority decisions. This system processes and synthesizes multiple data sources through structured workflows, producing objective priority ratings that reflect both the complexity of analysis and the need for actionable outcomes.
2Reliability
If context data from multiple sources is collected and analyzed, then the objectivity and comprehensiveness of priority ratings improve, but the processing time and system complexity increase
Solution Approach 1:
Context data from multiple sources is collected and pre-processed before priority rating decisions are made. The system proactively gathers software component information, user feedback, and historical data in advance, so that when prioritization is needed, the analysis can be performed quickly on already-processed information.
Solution Approach 2:
The prioritization system performs self-service by automatically collecting, processing, and analyzing context data without requiring manual intervention. The system autonomously synthesizes information from multiple sources and generates priority ratings, reducing both processing time and the need for human resources while maintaining high objectivity.
3Measurement precision
If manual analysis of issue context is performed, then the accuracy of priority ratings improves, but the resource allocation efficiency and scalability deteriorate
Solution Approach 1:
The system implements self-service prioritization by automatically collecting context data from multiple sources, analyzing the information using structured workflows, and generating priority ratings without manual intervention. This automated approach maintains high accuracy while enabling scalable processing of large volumes of issues.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated information processing system. Instead of human analysts manually examining issue contexts, the system uses automated workflows to collect, synthesize, and evaluate data from multiple sources, achieving both high accuracy and high throughput simultaneously.
4Ease of manufacture
If subjective priority ratings are used, then the implementation is simple and fast, but the resource allocation and workload management effectiveness deteriorate
Solution Approach 1:
The prioritization system operates autonomously by automatically collecting context data, analyzing it through structured workflows, and generating objective priority ratings. This self-service approach delivers actionable, data-driven priority assessments that improve resource allocation efficiency while maintaining ease of implementation through automation.
Solution Approach 2:
The system transforms the prioritization process by changing from subjective parameter assessment to objective parameter measurement. Priority ratings are derived from quantifiable context data including software component criticality, user feedback metrics, and historical performance data, enabling effective resource allocation while keeping the system straightforward to implement.
Data Source
AI summary
Systems and methods described herein relate to techniques for identifying and using context data to prioritize reported issues in a software context. A data record of a reported issue is accessed. The reported issue is associated with a software offering provided to a user. The data record comprises issue metadata that includes a first priority rating for the reported issue. The issue metadata is used to identify a relation between the reported issue and context data associated with the user. A second priority rating for the reported issue is generated based on at least the context data. The second priority rating may differ from the first priority rating. The second priority rating is presented at a computing device, optionally together with the first priority rating via a graphical user interface.