Veracode. has filed a patent for a method that involves updating a repository with detected flaws in software projects, obtaining candidate fixes from previous implementations and a machine learning model pipeline, presenting suggested fixes, and using selections for ongoing training of the machine learning model pipeline. GlobalData’s report on Veracode gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Veracode, Social data privacy protection was a key innovation area identified from patents. Veracode's grant share as of January 2024 was 47%. Grant share is based on the ratio of number of grants to total number of patents.
Software project flaw detection and fix suggestion method
The patent application (Publication Number: US20230409464A1) describes a method for software flaw remediation that involves updating a repository of detected flaws, obtaining candidate fixes from previous implementations and a machine learning model, presenting suggested fixes, and supplying training data for ongoing learning. The method also includes determining fix templates matching flaws, merging program code resulting from applying fix templates, comparing code submissions to identify fixes, and supplying training data based on selected fixes. The patent further details the use of machine-readable media with program code for tracking flaws, identifying candidate remediation code, presenting suggested remediations, and training a machine learning model pipeline to output fixes based on flaw type and structural context.
Additionally, the patent application outlines a system comprising a processor and machine-readable medium with program code for maintaining a repository of detected flaws, training a machine learning model pipeline, inputting vector representations of flaws, and supplying candidate program code fixes. The system also includes program code for preprocessing security code scan results, determining structural context of program code fixes, training deep learning and clustering algorithm models, and coupling the trained models to receive input. The system aims to improve software flaw remediation by utilizing machine learning models to generate effective fixes based on the structural context of program code fixes across different types of flaws, ultimately enhancing the efficiency and accuracy of the remediation process in software development pipelines.
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