Abstract: A system deploying a machine learning technique that utilizes known code graph and abstract syntax tree pairs for known JSON objects to learn a function for predicting a corresponding abstract syntax tree from a new JSON object. The predicted abstract syntax tree is used to generate code for formatting the new JSON object into a standardized data structure.

Abstract: Systems and methods are provided for implementing a machine learning (ML) model based compiler, language translator, and/or a decompiler. For example, the system may receive a first source code in a first programming language, tokenize the first source code file forming tokenized code, generate a sequence vector of tokenized code, and provide the sequence vector of tokenized code as input to a trained ML model compiler. The output of the trained ML model compiler may create a second executable file or the source code in a second programming language.

Abstract: An electronic control unit includes: a file acquisition portion that acquires a file to update software: and a storage portion that stores the file. A first virtual machine included in the electronic control unit includes: a determination portion that determines whether to transfer the file to a second virtual machine connected to the first virtual machine based on first safety integrity indicating a safety level of the first virtual machine and second safety integrity indicating a safety level of the second virtual machine; and a transfer portion that transfers the file to the second virtual machine when the determination portion determines to transfer the file to the second virtual machine.

Abstract: An electronic control unit includes: a file acquisition portion that acquires a file to update software; and a storage portion that stores the file. A first virtual machine included in the electronic control unit includes: a determination portion that determines whether a first hypervisor that manages the first virtual machine and a second hypervisor that manages a second virtual machine connected to the first virtual machine are the same; and a transfer portion that transfers the file to the second virtual machine when the determination portion determines that the first hypervisor and the second hypervisor are different from each other.

Abstract: A method for generating a user interface is described. A script file for a software component is received, the software component being configured by source code that specifies a plurality of variables for the software component. The script file is parsed for markup language that describes a graphical user interface for the plurality of variables of the software component. A graphical user interface for modifying values of the plurality of variables of the software component is automatically generated using the markup language.

Abstract: A system may include a cluster and a module of the cluster. The module may include a user resource definition and a catalog server. The catalog server may maintain a configuration of the cluster.

Abstract: Disclosed in some examples, are systems, methods, devices, and machine readable mediums which use improved dynamic programming algorithms to pack conditional branch instructions. Conditional code branches may be modeled as directed acyclic graphs (DAGs) which have a topological ordering. These DAGs may be used to construct a dynamic programming table to find a partial mapping of one path onto the other path using dynamic programming algorithms.

Abstract: Techniques for tokenizing software applications (apps) and for providing application functionality using webpage non-fungible tokens (NFTs). A method includes encoding a webpage associated with apps into portions of code. Each portion of code causes tasks of the respective apps to be performed when executed by a device. The method also includes creating a database including at least webpage nodes and NFT nodes in which the encoded portions of code are stored among the nodes, one of the webpage nodes is associated with the webpage, one or more of the NFT nodes is connected to the webpage node that is associated with the webpage, and each of the NFT nodes corresponds to one of the apps. The method also includes providing the portions of code to a device authorized by an owner of the NFT node, thereby delivering the code for providing application functionality to the authorized device.

Abstract: Refactoring a monolithic computer application can include transforming textual input into context-aware tokens represented by machine-processable data structures, the textual input acquired from text associated with a computer application having a monolithic architecture for implementing one or more application processes. Based on co-occurrence frequencies among the context aware tokens, one or more groupings of context-aware tokens can be determined. An association between each grouping and a code construct can be determined. Invocation sequences based on time series analyses of computer-generated usage data generated in response to execution of the one or more application processes can be generated, each invocation sequence linking two or more code constructs based on a time series analysis linking groupings that correspond to the linked code constructs.

Abstract: An automatic SDK testing case generation system includes a computer software application for automatically generating optimized test cases. The automatic SDK testing case generation system categorizes candidate APIs presented by an SDK to be tested. The categories conform to an API call category sequence. The system iterates through API calling paths, which conform to the API call category sequence, to generate optimized subsets of test cases. The combination of the optimized subsets of test cases forms the final set of test cases for being used to test the SDK.

Abstract: A compile method for a neural network, the compile method includes receiving data related to the neural network, generating a grouped layer by grouping layers comprised in the neural network based on the data, generating a set of passes executable in parallel based on a dependency between a plurality of passes to process the neural network, generating a set of threads performing a plurality of optimization functions based on whether optimization operations performed by the optimization functions is performed independently for the layers, respectively, or sequentially based on a dependency between the layers, and performing compilation in parallel based on the grouped layer, the set of passes, and the set of threads.

Abstract: Systems and methods are described that implement a tool chain which receives original software source code, analyzes the code and divides the code into modules that run optimally on the available heterogeneous resources. For example, the toolchain system segments original source code into code segments, and determine the specialized processor resource, such as a digital signal processing (DSP) processor, Field Programming Gate Array (FPGA), Graphical Processing Unit (GPU), and the like, that most optimally performs computations of the particular code segment. A parsing engine determines the processor of the heterogenous resources, based on a set of rules and/or a trained classifier (e.g., a trained machine learning model). New code segments can be generated that can be executed on the determined type of processor. Further, the system enables application programming interfaces (APIs) that can interface the new code segment with other generated code segments and/or some portions of the original code.

Abstract: By analyzing transaction data of an executing application, an application graph is constructed, the application graph comprising a plurality of nodes and a plurality of edges connecting pairs of the plurality of nodes, a node in the application graph corresponding to a module of the application. The plurality of nodes is clustered into a set of clusters. Formation of a cluster in the set of clusters is analyzed, the analyzing identifying a central node of the cluster, a feature importance in placing a node into the cluster, and an edge importance in placing the node into the cluster. Responsive to a confidence value in the cluster being above a threshold confidence value, using the central node of the cluster, the application is refactored into a set of microservices, the cluster corresponding to a microservice, the central node of the cluster corresponding to a component of the microservice.

Abstract: Systems and methods are provided for automated computer code editing. The method includes training a code-editing neural network model using a corpus of code editing data samples, including the pre-editing samples and post-editing samples, and parsing the pre-editing samples and post-editing samples into an Abstract Syntax Tree (AST). The method further includes using a grammar specification to transform the AST tree into a unified Abstract Syntax Description Language (ASDL) graph for different programming languages, and using a gated graph neural network (GGNN) to compute a vector representation for each node in the unified Abstract Syntax Description Language (ASDL) graph. The method further includes selecting and aggregating support samples based on a query code with a multi-extent ensemble method, and altering the query code iteratively using the pattern learned from the pre- and post-editing samples.

Abstract: Examples of the present disclosure provide apparatuses and methods for target architecture determination. An example method comprises receiving an indication of a type of target architecture in a portion of source code and creating compiled code for the type of target architecture based on the indication.

Abstract: A Flutter-based compiling method, a compiling device, an executing method, a computer-readable storage medium, and a computer device are provided. The Flutter-based compiling method includes: receiving configuration content; in response to the configuration content, compiling and generating an executable file, where the executable file includes at least two of a Native component, a Flutter Native component and a Flutter dynamic component, and is configured to generate a routing table during operation, to enable the Native component, the Flutter Native component and the Flutter dynamic component to communicate with each other through the routing table.

Abstract: Static and automatic realization of inter-basic block burst transfers for high-level synthesis can include generating an intermediate representation of a design specified in a high-level programming language, wherein the intermediate representation is specified as a control flow graph, and detecting a plurality of basic blocks in the control flow graph. A determination can be made that plurality of basic blocks represent a plurality of consecutive memory accesses. A sequential access object specifying the plurality of consecutive memory accesses of the plurality of basic blocks is generated. A hardware description language (HDL) version of the design is generated, wherein the plurality of consecutive memory accesses are designated in the HDL version for implementation in hardware using a burst mode.

Abstract: A vehicle control apparatus including: a storage portion configured to store therein a vehicle control software for controlling a vehicle; a receiving portion configured to receive an update software from an external server; and an updating portion configured to update the vehicle control software stored in the storage portion, to the update software received by the receiving portion. The updating portion is configured to determine whether the vehicle is in a long-time requiring state that requires a long time for making a repair of the vehicle which is needed in case update of the vehicle control software is not successfully completed. The updating portion is configured to execute the update of the vehicle control software when determining that the vehicle is not in the long-time requiring state, and is configured to withhold the update of the vehicle control software when determining that the vehicle is in the long-time requiring state.

Abstract: Even when one refactoring operation cannot establish a target software structure, an appropriate refactoring operation establishes the target software structure. An improvement proposing device includes: a structure comparator to output, as an improvement object, a difference between a first software structure and a second software structure different in software structure from the first software structure; and an improvement plan examining unit to examine an improvement plan for each improvement portion in the improvement object, the improvement plan being a method for bringing the first software structure closer to the second software structure.

Abstract: A vehicle control apparatus including: a storage portion configured to store therein a vehicle control software for controlling a vehicle; an updating portion configured to update the vehicle control software stored in the storage portion, to an update software, and an operation check portion configured, when the update software is stored in the storage portion, to make an operation check as to whether the vehicle operates normally or not, by executing processing of the update software on condition that the vehicle is in a vehicle stopped state in which the vehicle is suppressed from being moved in forward and reverse directions. When it is checked by the operation check portion that the vehicle operates normally with the processing of the update software, the updating portion is configured to update the vehicle control software to the update software.