Disclosed embodiments relate to systems and methods for discovering and remediating hidden secrets in code files. Techniques include accessing an element of source code for analysis, performing a static analysis of the element of source code, and generating a control flow representation that identifies a plurality of functions and a sequence of functions associated with the element of the source code. The techniques may further include determining a probability of a hidden secret being included in the element of source code and performing a security action of at least one of: generating an alert, displaying a visual indication of the probability, generating a report associated with the hidden secret, sending data associated with the probability to a machine learning system for training the machine learning system, or a remediating action associated with the hidden secret.

A method, computer system, and a computer program product for project development in a collaborative platform is provided. The present invention may include identifying one or more features of an open source project. The present invention may include analyzing the one or more features of the open source project and assigning a sentiment to each of the one or more features. The present invention may include generating a requirement mapping for the open source project, wherein the requirement mapping is a series of the one or more features.

A method, computer system, and a computer program product for project development in a collaborative platform is provided. The present invention may include identifying one or more features of an open source project. The present invention may include analyzing the one or more features of the open source project and assigning a sentiment to each of the one or more features. The present invention may include generating a requirement mapping for the open source project, wherein the requirement mapping is a series of the one or more features.

Source code is analyzed to identify components. The components are each assigned a complexity score. Documentation for the source code is identified, related to the components, and given a score based on the quantity of the documentation for the component and the complexity score for the component. To determine semantic meaning of the documentation, vector embeddings for the documentation languages may be generated and aligned. Alignment causes the different machine learning models to generate similar vectors for semantically similar words in the different languages. Since the vectors of the words of the other languages are similar to the vectors of the words in a primary language with similar meanings, the vector representation of the documentation in the other languages will match the vector representation of the source code when the documentation is substantially on the same topic.

Source code is analyzed to identify components. The components are each assigned a complexity score. Documentation for the source code is identified, related to the components, and given a score based on the quantity of the documentation for the component and the complexity score for the component. To determine semantic meaning of the documentation, vector embeddings for the documentation languages may be generated and aligned. Alignment causes the different machine learning models to generate similar vectors for semantically similar words in the different languages. Since the vectors of the words of the other languages are similar to the vectors of the words in a primary language with similar meanings, the vector representation of the documentation in the other languages will match the vector representation of the source code when the documentation is substantially on the same topic.

A machine learning model is trained to translate source code from one or more programming languages into a common programming language. The machine learning model translates source code from the other languages into the common programming language. A language embedder generates a vector for each function in the source code, all of which is now in the common programming language. A user provides a text search query which is converted by a language embedder to a vector. Based on the vector of the text search query and the vectors for the source code, search results are generated and presented in a user interface. Additional machine learning models may be trained and used to measure function complexity, test coverage, documentation quantity and complexity, or any suitable combination thereof. These measures may be used to determine which search results to present, an order in which to present search results, or both.

A machine learning model is trained to translate source code from one or more programming languages into a common programming language. The machine learning model translates source code from the other languages into the common programming language. A language embedder generates a vector for each function in the source code, all of which is now in the common programming language. A user provides a text search query which is converted by a language embedder to a vector. Based on the vector of the text search query and the vectors for the source code, search results are generated and presented in a user interface. Additional machine learning models may be trained and used to measure function complexity, test coverage, documentation quantity and complexity, or any suitable combination thereof. These measures may be used to determine which search results to present, an order in which to present search results, or both.

Translations between versions of data object schemas may be performed between data object producers and data object consumers. A request to update a data object may be an update according to an additional version of a schema applicable to the data object from an existing version of a schema. The data object may be provided by one component of a distributed system. A trigger event may be detected to send the data object to a different component of the distributed system. The data object may be translated from the additional version to the existing version and sent to the different component.

Translations between versions of data object schemas may be performed between data object producers and data object consumers. A request to update a data object may be an update according to an additional version of a schema applicable to the data object from an existing version of a schema. The data object may be provided by one component of a distributed system. A trigger event may be detected to send the data object to a different component of the distributed system. The data object may be translated from the additional version to the existing version and sent to the different component.

Some embodiments of the invention provide a method for defining code-based policies. The method generates a policy-builder first view of a policy for display in a graphical user interface (GUI) by processing a syntax tree that is generated from a code second view of the policy. The method receives, through the policy-builder first view, a modification to a portion of the policy. To reflect the modification, the method updates a portion of the syntax tree that corresponds to the portion of the policy that is affected by the modification. Based on the updating of the syntax tree, the method updates the code second view by modifying a portion of the code second view that corresponds to the updated portion of the syntax tree.