Novel tools and techniques are provided for implementing object-oriented infrastructure-as-code platform (“OOIACP”) and its functionalities. In various embodiments, an OOIACP may receive a request to perform a function from a requesting device. The OOIACP includes a declarative configuration language (“DCL”)-based infrastructure-as-code (“IAC”) software tool that provides structure and data functionalities and a wrapper tool that provides algorithm and sequence functionalities to the IAC software tool to convert the IAC software tool into an object-oriented programming (“OOP”)-based IAC system. The OOIACP uses a run command to perform the requested function, by identifying classes within a class hierarchy associated with the requested function, and initiating a set of procedures in each identified class. Each class and subclass within the class hierarchy has one or more predefined class behaviors, and each subclass inherits class behaviors of classes to which it belongs and of any intermediate subclasses.

An intent compiler is disclosed. The intent compiler includes a backend services layer having at least one service application programming interface (API) specification. The intent compiler also includes a service adapter layer to receive the at least one service API specification and automatically generate at least one service adapter based on the at least one service API specification. The intent compiler additionally includes an application layer to automatically generate an application.

A hardware environment-based data quantization method includes: parsing a model file under a current deep learning framework to obtain intermediate computational graph data and weight data that are independent of a hardware environment; performing calculation on image data in an input data set through a process indicated by an intermediate computational graph to obtain feature map data; separately performing uniform quantization on the weight data and the feature map data of each layer according to a preset linear quantization method, and calculating a weight quantization factor and a feature map quantization factor (S103); combining the weight quantization factor and the feature map quantization factor to obtain a quantization parameter that makes hardware use shift instead of division; and finally, writing the quantization parameter and the quantized weight data to a bin file according to a hardware requirement so as to generate quantized file data (S105).

A neural network model conversion method, a server, and a storage medium are provided according to embodiments of the present disclosure. The neural network model conversion method includes: parsing a neural network model to obtain initial model information; reconstructing the initial model information to obtain streaming model information; generating a target model information file according to the streaming model information; and running, under a streaming architecture, the neural network model according to the target model information file.

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.

Systems and methods for iterating between a graphical user interface and an expression for data visualization. Exemplary methods include: receiving an edited expression from a user, the edited expression including changes to the expression and being associated with a component; evaluating the edited expression; displaying the component using the evaluation of the edited expression; determining a user interface block using the edited expression; and presenting the user interface block to the user in a graphical user interface.

Methods, devices and media for two-pass source code transformation from a first high-level programming language to a second high-level programming language are described. Two different source code transformation technologies are combined to produce a two-pass source code transformation method: a compiler-based source code transformation technique is used in a first pass, and a parse-tree-based source code transformation technique is used in second pass. The second pass may be used to automatically refactor the source code to enhance desired properties of the second programming language. A two-pass C-to-Rust transformation technique, CRustS, is described which automatically generates Rust source code that exhibits memory safety and overcomes other limitations of existing tools such as C2Rust.

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.

Provided is an automatic event graph construction method for multi-source vulnerability information. The method includes the following steps. A vulnerability report is crawled from a vulnerability database, a cause of vulnerability is taken as an event trigger word, and a vulnerability type is determined through the cause of vulnerability. An attacker, consequence, location and other information in a description are identified by named-entity recognition, and information completion is performed. An explicit relation between events is extracted by using text information, an implicit relation between events is extracted by using text similarity, and vulnerability-related code representation is performed. Obtained vulnerability event information is visualized into an event graph through a visualization tool.

Code translation is an evolving field and due to advancements in the infrastructure and compute power. The existing methods for code translation are time and effort intensive. A method and system for translation of codes based on the semantic similarity have been provided. A machine learning model is developed, that understands and encapsulates the semantics of the code in the source side and translates the semantic equivalent code which is more maintainable and efficient compared to one to one translation. The system is configured to group a plurality of statements present in the source code together into blocks of code and comprehend the semantics of the block. The system is also trained to understand syntactically different but semantically similar statements. While understanding the semantics of the block and translating, the unused/duplicate code etc. gets eliminated. The translated code is better architected and native to the target environment.