Abstract: Systems and methods for validating the accuracy of an authenticated, end-to-end, digital response system are provided. Methods may include curating a database of training data, including historical profile data and historical interaction data. Profile data may include a name, an identifier, and a set of financial instruments for a plurality of system users. Interaction data may include records of multi-step interactions between the system users and the digital response system. Methods may include generating, via a machine-learning (ML) engine and based on the training data: a test profile including a fictitious name, a fictitious identifier, and a fictitious set of financial instruments; authentication data for the test profile including a username and password that are operational for authenticating the test profile to the digital response system; and a simulated conversation for the test profile including an utterance that is associated with an intended request.

Abstract: A method for automatically providing library package recommendations is disclosed. The method includes aggregating, via a communication interface, sets of source code from a repository, each of the sets of source code including a code snippet that relates to a portion of source code for a software program; initiating, by using a model, code context analysis of the code snippet to identify an alternative library package and a replaceable library package; performing, by using the model, implemented library analysis of the code snippet to determine a complementary library package; generating recommendations for the code snippet, the recommendations including information that relates to the complementary library package, the alternative library package, and the replaceable library package; and associating, in the repository, the generated recommendations with the code snippet.

Abstract: Systems and methods for automated code analysis and tagging are disclosed. In one embodiment, a method for automated code analysis and tagging may include: (1) receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase; (2) parsing, by the code annotation computer program, the training code snippet into a data structure; (3) quantifying, by the code annotation computer program, the data structure, (4) parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords; (5) quantifying, by the code annotation computer program, the plurality of keywords; and (6) training, by the code annotation computer program, a code annotation model based on a similarity between the quantified data structure and a smoothing parameter for a Dirichlet prior smoothing estimate.

Abstract: A method for automatically providing library package recommendations is disclosed. The method includes aggregating, via a communication interface, sets of source code from a repository, each of the sets of source code including a code snippet that relates to a portion of source code for a software program; initiating, by using a model, code context analysis of the code snippet to identify an alternative library package and a replaceable library package; performing, by using the model, implemented library analysis of the code snippet to determine a complementary library package; generating recommendations for the code snippet, the recommendations including information that relates to the complementary library package, the alternative library package, and the replaceable library package; and associating, in the repository, the generated recommendations with the code snippet.