Abstract: A method for facilitating identification of secrets in source code by using machine learning is provided. The method includes retrieving a plurality of files from a repository, each of the plurality of files including a source code file; parsing the source code file to identify a training feature; associating a predetermined label with the training feature, the predetermined label corresponding to a secret label and a non-secret label; training a model by using the training feature and the corresponding predetermined label; receiving, via a graphical user interface, a test file, the test file including a set of source codes; parsing the set of source codes to identify a feature; and determining, by using the model, a first characteristic of the feature.

Abstract: A method may include: retrieving a plurality of code snippets from code repositories; generating a syntax representation, a property representation for each of the code snippets; receiving a query comprising a query code snippet, natural language keywords, and/or a string pattern; performing string-based matching and parser/syntax tree matching on the query and the tree representations, syntax matching on the query and the syntax representations, and property matching on the query and the property representations, wherein each of the matchings results in a score; combining the scores of the string-based matching, the parser/syntax tree matching, the syntax matching, and/or the property matching; identifying a plurality of code snippets of interest based on the combined scores; classifying the code snippets of interest using a machine learning classifier; outputting a list of the code snippets of interest with their classifications; and training the machine learning classifier based on user feedback.

Abstract: A method for facilitating identification of secrets in source code by using machine learning is provided. The method includes retrieving a plurality of files from a repository, each of the plurality of files including a source code file; parsing the source code file to identify a training feature; associating a predetermined label with the training feature, the predetermined label corresponding to a secret label and a non-secret label; training a model by using the training feature and the corresponding predetermined label; receiving, via a graphical user interface, a test file, the test file including a set of source codes; parsing the set of source codes to identify a feature; and determining, by using the model, a first characteristic of the feature.