Automated Defect Categorization Using Keyword Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current manual processes for categorizing software defects are time-consuming, prone to errors, and resource-intensive, making it difficult to identify patterns and trends, and require repeated analysis as new defects emerge, which complicates upgrade planning and patch management for software development and maintenance teams.
Innovation Solution
A method and system for automatically classifying defects using keywords to determine categorization, which accesses databases to identify relevant information, determines categorization scores, and stores results, enabling efficient defect analysis and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual processes are used for categorizing defects, then categorization can be performed with simple tools, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables self-service automated defect categorization by using machine learning models that automatically analyze defect data, extract patterns, and assign categories without requiring manual intervention from developers or maintenance teams
Solution Approach 2:
The patent replaces manual mechanical categorization processes with computational systems that use machine learning algorithms, natural language processing, and automated data analysis to perform defect classification, substituting human cognitive work with automated computational mechanisms
2Productivity
If manual categorization is performed, then the system is simple to operate, but errors increase and productivity decreases
Solution Approach 1:
The system implements feedback mechanisms where categorization results are continuously evaluated, and the machine learning models are retrained with new defect data and correction feedback, improving accuracy over time while maintaining high-speed automated processing
Solution Approach 2:
The system performs preliminary automated categorization before human review, pre-classifying defects using machine learning models to reduce the burden on manual reviewers and ensure consistent initial classification across all defect data
3Loss of information
If unstructured defect data is stored in databases, then data storage is simple, but information retrieval and analysis become difficult
Solution Approach 1:
The system segments unstructured defect data into structured components including defect type, severity, functional area, and associated keywords, organizing the data into categorical segments that can be easily retrieved and analyzed while preserving the original unstructured information
Solution Approach 2:
The patent creates a universal data structure that serves multiple functions: storing raw defect information, enabling pattern analysis, supporting trend identification, and facilitating automated categorization, allowing the same data infrastructure to support diverse analytical needs
4Duration of action of stationary object
If repeated manual analysis is performed as new defects emerge, then the analysis can be thorough, but the time and resource consumption increases
Solution Approach 1:
The system enables continuous automated defect analysis that operates continuously as new defects emerge, with machine learning models continuously processing defect data, updating patterns, and providing real-time insights without requiring repeated manual analysis cycles
Solution Approach 2:
The patent implements dynamic categorization where the system adapts to changing defect patterns over time, with machine learning models that evolve and update their classification rules based on new data, allowing the analysis to remain thorough while maintaining high throughput
Data Source
AI summary
Disclosed are method(s), system(s), and computer program product(s) for automatically categorizing a defect into a category based at least on a keyword related to the defect. The method identifies information regarding the defect and identifies additional information related to categorizing the defect. The method identifies a keyword and determines whether the defect may be categorized into a category using the keyword. The method further comprises determining if the result of categorization is deterministic. In some embodiments, the method or the system further comprises associating a first score for categorizing the defect into the category based on the keyword and a second score for categorizing the defect into the category based on another keyword. In some embodiments, the method or the system further comprises identifying or determining a relationship between the first score and the second score with respect to the first category for the defect.