17985345. METHOD, ELECTRONIC DEVICE, AND PROGRAM PRODUCT FOR BUG CLASSIFICATION simplified abstract (Dell Products L.P.)
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 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 17985345 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, 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.
- Bug classification method using trained computing models
- Presentation of bug classification information based on user interaction
- Dynamic adjustment of computing models through retraining for improved 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 prone to errors. 2. Ensuring accuracy in bug classification can be challenging without automated tools.
Benefits
1. Increased efficiency in bug processing. 2. Improved accuracy in bug classification. 3. Dynamic adjustment of computing models for better performance.
Potential Commercial Applications
Automated bug classification technology can be utilized by software development companies, quality assurance teams, and product testing departments to streamline bug processing workflows and enhance product quality.
Possible Prior Art
One possible prior art in bug classification technology is the use of machine learning algorithms to automate bug categorization based on historical data and bug descriptions.
Unanswered Questions
How does the retraining process work in adjusting the computing models for bug classification?
The article does not provide specific details on the retraining process and the criteria used to determine when retraining is necessary.
What types of bugs or software systems can be effectively classified using this method?
The article does not specify the scope or limitations of the bug classification method in terms of the types of bugs or software systems it can effectively classify.
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.
