Code Defect Detection via Log Analysis and Memory Image
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Solution Overview
Problem
Current methods for detecting code defects in software products are inefficient, requiring extensive human resources and analysis time, as defect tracking tools cannot effectively assist testers and developers in identifying and repairing defects.
Innovation Solution
A method that determines log information associated with defects during testing, classifies the nature of the defect using trained classification models, and identifies target code in the software product's code that causes the defect, allowing for targeted analysis strategies based on the defect's nature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual defect analysis is performed by testers and developers, then defect detection can be conducted, but extensive human resources and analysis time are consumed
Solution Approach 1:
The system enables self-service defect analysis by automatically collecting log information, classifying defect natures, and identifying target code segments without requiring manual intervention. The defect analysis system performs self-diagnosis through automated processing of test logs and memory images, reducing dependency on human analysts while maintaining detection accuracy
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computer-based system. The system substitutes human testers and developers with automated algorithms that classify defect natures and identify problematic code segments, transforming manual labor into an automated computational process that reduces time consumption while preserving detection capability
2Reliability
If comprehensive defect analysis is performed on all reported defects, then thorough defect detection is achieved, but resource consumption increases
Solution Approach 1:
The patent segments the defect analysis process into distinct classification stages based on defect natures (code defects, test code defects, environmental factors). By dividing the analysis into targeted segments rather than uniform comprehensive analysis, the system applies appropriate processing depth to each segment type, reducing overall resource consumption while maintaining thoroughness for critical code-related defects
Solution Approach 2:
The system applies different analysis depths and strategies to different defect segments based on their nature. For code-related defects, comprehensive analysis is performed to ensure thoroughness, while for environmental factors or test code issues, lighter analysis is applied. This local differentiation of quality ensures reliability where needed while optimizing resource allocation
3Loss of time
If automated defect analysis is implemented, then analysis time is reduced, but complexity of the detection system increases
Solution Approach 1:
The patent introduces classification models as intermediary components between raw log data and final defect identification. These intermediary models simplify the overall system architecture by breaking down the complex analysis task into manageable classification stages, making the automated system more structured and maintainable while achieving rapid analysis
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for code defect detection. The method described here includes determining log information associated with a defect based on the defect reported during testing of a software product. The method further includes determining a nature of the defect based on the log information. The method further includes determining, based on the nature, the log information, and a memory image file generated when the defect is reported, target code in code of the software product that causes the defect, in response to the nature indicating that the defect is caused by the code of the software product and needs to be repaired. By using the solution of the present application, different analysis strategies for defects may be adopted based on natures of the defects, thereby improving the efficiency of detecting code defects.