Dell products l.p. (20240134784). METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR BUG CLASSIFICATION simplified abstract
Contents
- 1 METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR BUG CLASSIFICATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR BUG CLASSIFICATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR BUG CLASSIFICATION
Organization Name
Inventor(s)
METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR BUG CLASSIFICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240134784 titled 'METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR BUG CLASSIFICATION
Simplified Explanation
Embodiments of the present disclosure provide a method, an electronic device, and a medium for bug classification. The method includes determining classification information of a bug based on description information generated during product testing, presenting the classification information, determining if the performance of the computing model satisfies a predetermined condition based on user interaction, and retraining the computing model if the performance does not meet the condition. This allows for automatic bug classification and dynamic adjustment of the computing model for improved accuracy and efficiency in bug processing.
- Bug classification method based on description information
- Presentation of bug classification information
- User interaction for determining model performance
- Retraining of computing model for improved bug classification accuracy and efficiency
Potential Applications
The technology can be applied in software development and quality assurance processes to automate bug classification and improve bug processing efficiency.
Problems Solved
1. Manual bug classification can be time-consuming and error-prone. 2. Ensuring accuracy in bug classification can be challenging without automated tools.
Benefits
1. Automatic bug classification saves time and reduces human error. 2. Dynamic adjustment of computing models improves bug classification accuracy. 3. Increased efficiency in bug processing leads to faster product development cycles.
Potential Commercial Applications
Automated bug classification technology can be utilized by software development companies, quality assurance teams, and product testing organizations to streamline bug processing and improve overall product quality.
Possible Prior Art
One possible prior art in bug classification technology is the use of machine learning algorithms to automatically categorize and prioritize software bugs based on their severity and impact on the system.
Unanswered Questions
How does the user interaction impact the retraining process of the computing model?
The specific criteria for user interaction triggering the retraining of the computing model are not detailed in the abstract. Further information on this aspect would provide a clearer understanding of the system's functionality.
What types of bugs can be classified using this method?
The abstract does not specify the scope of bugs that can be classified using this technology. Understanding the limitations and capabilities of the bug classification method would be beneficial for potential users.
Original Abstract Submitted
embodiments of the present disclosure provide a method, an electronic device, and a medium for bug classification. the method includes determining, based on description information of a bug generated during product testing, classification information of the bug through at least one trained computing model; presenting the classification information of the bug; determining, based on user interaction for the presented classification information, whether performance of the at least one computing model satisfies a predetermined condition; and determining that the at least one computing model needs to be retrained in response to determining that the performance of the at least one computing model does not satisfy the predetermined condition. in this way, automatic classification of the bug is realized, and the computing model can be dynamically adjusted by retraining, so as to ensure accuracy of the automatic classification and improve efficiency of bug processing.
