Sap Se

Marco Rosa

Slim Trabelsi

Merve Sahin

Sofiane Lounici

Carlo Maria Negri

Caroline Banchereau

In an example embodiment, various types of logging inconsistencies contained in a source code repository are automatically detected and reported to the developers responsible for the maintenance of the software products. First, logging statements in the source code are identified, such as by using static code analysis tools (with some help if custom logging libraries or logging classes are used). For each logging statement, the function (e.g., the method) and/or the block of code in which the log resides is identified. Second, logging statements and their methods are analyzed pairwise in order to find the logging statements and the functions (or code blocks) that are similar to each other. In an example embodiment, this analysis is performed by a machine learning model, as will be described in more detail later.

Automatic identification of logging inconsistencies in source code

In an example embodiment, a solution is provided for an automated penetration testing system using pre-trained large language models (LLMs), which come equipped with a knowledge base of existing system configurations and commands. The knowledge base of these pre-trained LLMs may be extended with new training data, thereby updating their knowledge with new configuration commands, systems, and so forth.

Generative artificial intelligence penetration testing automation

Source code is scanned to generate a list of vulnerable tokens. Thereafter, the list of vulnerable tokens is inputted into a machine learning model to identify false positives in the list of vulnerable tokens. Based on this identification, the list of vulnerable tokens can be modified to remove the identified false positives. Related apparatus, systems, techniques and articles are also described.

Growing community of inventors

Biot, France

Marco Rosa