Sample-difference-based method and system for interpreting deep-learning model for code classification
US12393404B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 27, 2023 |
| Grant date | Aug 19, 2025 |
| Priority date | — |
| Expiry date | Mar 14, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F8/42
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A sample-difference-based method and system for interpreting a deep-learning model for code classification is provided, wherein the method includes a step of off-line training an interpreter: constructing code transformation for every code sample in a training set to generate difference samples; generating difference samples respectively through feature deletion and code snippets extraction and then calculating feature importance scores accordingly; and inputting the original code samples, the difference samples and the feature importance scores into a neural network to get a trained interpreter; and a step of on-line interpreting the code samples: using the trained interpreter to extract important features from the snippets, then using an influence-function-based method to identify training samples that are most contributive to prediction, comparing the obtained important features and the most contributive training samples, and generating interpretation results for the object samples. The inventive system includes an off-line training module and an on-line interpretation module.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.