Machine learning methods for source code quality analysis
US12197913B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 7, 2020 |
| Grant date | Jan 14, 2025 |
| Priority date | — |
| Expiry date | Dec 7, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A preferably cloud-based platform supports improvements in software development by assessing quality of source code files, for example, when files are pushed to a software repository. Various static analysis tools are executed on a source file, and the resulting bug reports, which reflect native features of the tools are assembled in a dataset. The bug dataset is enhanced by adding additional features that are not natively generated by the static analysis tool. An ML classifier is trained to predict a selected bug feature, and the classifier is used to update the bug dataset to include estimated values of the selected feature. In an embodiment, post-processing analysis of bug report datasets applies machine learning methods to predict the “severity” of bug reports, an indication of whether they are likely to be true or false. Further, a report of code quality can be returned based on the severity predictions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.