Abstract: Systems and methods for unit test generation using reinforcement learning augmented transformer architectures are disclosed. A method may include: receiving raw data for source code from a database; identifying a function for which a unit test will be generated and an existing unit test for that function; receiving the function and the existing unit test; generating a generated unit test for the function using the function for the unit test and the existing unit test using a deep learning model; applying a loss function to the generated unit test, wherein the loss function is based on a comparison between the generated unit test and the existing unit test and results of the application of the loss function are fed back to the transformer computer program; simulating the generated unit test using a simulator; generating scalar feedback; and refining the generated unit test using the scalar feedback.

Abstract: Systems and methods for auto-thresholding using pairwise feature cross-correlation for hyperparameter value selection are disclosed. A method may include a hyperparameter value optimization computer program: receiving data to be used by a clustering algorithm; receiving a selection of a hyperparameter value to tune; for each possible hyperparameter value, executing the clustering algorithm resulting in a set of clusters for each hyperparameter value; extracting a series of cluster features from the set of clusters; performing pairwise cross-correlation on the series of cluster features resulting in potential candidates for an optimal hyperparameter value; aggregating maximum or minimum values for the hyperparameter value at their respective indices; selecting an optimum value for the hyperparameter value; and outputting the optimum value for the hyperparameter value to the clustering algorithm.

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: Systems and methods for detecting code duplication are disclosed. In one embodiment, a method for detecting exact code snippet duplicates may include: (1) representing, by a code duplication detection computer program, each of a plurality of code snippets in a codebase as an abstract syntax trees; (2) featurizing, by the code duplication detection computer program, the abstract syntax trees into corpus feature vectors by converting the abstract syntax tree into vector representations; (3) generating, by the code duplication detection computer program, dense feature vectors from the corpus feature vectors using a dimension reduction technique; (4) identifying, by the code duplication detection computer program, exact duplicate code snippet matches by apply density-based clustering to the dense feature vectors; and (5) tagging, by the code duplication detection computer program, the exact duplicate code snippets.

Abstract: Systems and methods for source code understanding using spatial representations are disclosed. In one embodiment, a method may include: (1) receiving, by a source code understanding computer program, a source code snippet; (2) converting, by the source code understanding computer program, the source code snippet to a two-dimensional image representation using an encoding technique; (3) mapping, by the source code understanding computer program, the two-dimensional image representation into a three-dimensional image representation; (4) determining, by the source code understanding computer program, a classification for the source code snippet using a deep learning network; and (5) providing, by the source code understanding computer program, the classification for the source code snippet to a downstream system.

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.