Deterministic memory allocation for real-time applications. In an embodiment, bitcode is scanned to detect calls by a memory allocation function to a dummy function. Each call uses parameters comprising an identifier of a memory pool and a size of a data type to be stored in the memory pool. For each detected call, an allocation record, comprising the parameters, is generated. Then, a header file is generated based on the allocation records. The header file may comprise a definition of bucket(s) and a definition of memory pools. Each definition of a memory pool may identify at least one bucket.

Generating test input values for functional components based on test coverage analysis is disclosed herein. In one example, a processor device of a computing device performs a test coverage analysis of a plurality of programming instructions of a functional component that takes a plurality of input parameters. Based on the test coverage analysis, the processor device identifies a subset of the programming instructions for which testing has not been performed. The processor device then identifies a data type for each of the functional component's input parameters, and generates test input values based on the data types and the test coverage analysis, including a test input value that causes executable instructions corresponding to the subset of programming instructions to be executed during subsequent testing. The test input values may be used during subsequent unit testing to ensure full test coverage of the functional component.

A method is provided for creating and executing a control program for controlling an automation system having a controller and a web server connected to the controller. The method includes creating a first version of a program code of a control program for the automation system in an input module of a web-based development environment executed in a web browser, in a code creating step; executing a translation module of the web-based development environment on the web server and translating the program code into a program code of a binary language, in a translating step; and executing the program code in the binary language with the aid of the controller of the automation system, in an executing step. An automation system is also provided.

It is disclosed a PLC Program analysis method where a program is translated into a program model in a logical framework, from which properties are determined. Said properties coupled with interlocking properties are verified by an automated solver. If contraposition of a property is satisfiable, counter-examples representative of model's inputs and internal memory values is provided. Counter-examples are translated into error initial configurations of said model. Execution of the model is simulated with said model error initial configurations, and error intermediary configurations of said model simulation are recorded up to said property violation. Error initial and intermediary configurations of said original program are derived from error initial configurations of said model and error intermediary configurations of said model simulation and displayed. An apparatus for executing said method is provided.

According to an aspect of an embodiment, operations include receiving a set of NL descriptors and a corresponding set of PL codes. The operations further include determining a first vector associated with each NL descriptor and a second vector associated with each PL code, using language models. The operations further include determining a number of a set of semantic code classes to cluster the set of PL codes into the set of semantic code classes, based on the number, the first vector, and the second vector. The operations further include training a multi-class classifier model to predict a semantic code class, from the set of semantic code classes, corresponding to an input NL descriptor. The operations further include selecting an intra-class predictor model based on the predicted semantic code class. The operations further include training the intra-class predictor model to predict a PL code corresponding to the input NL descriptor.

The present invention provides a general description language data system for directed acyclic graph automatic task flow, including: Step definition layer, Workflow definition layer and Template definition layer; The Step definition layer is the description of a single task, for the input and output declarations of each docker image or other executor, comprises name, type, file and parameters. The Workflow definition layer is a workflow composed of one or more Steps, the dependency topology of these Steps needs to be defined, and shared parameters can also be defined. The Template definition layer is based on a Workflow definition layer. The Template definition layer pre-sets the parameters, and supplies the descriptions, checkers or data source definitions of the parameters. The data center of the present invention is used with the task execution tool, and a programming language needs to be used to implement the corresponding tool.

A lexical analyzer is provided to analyze serverless application code to help ensure that the serverless application is portable between different execution environments. The lexical analyzer may identify non-portable features of the application, and alerts of these features may be provided to users. A transfer tool may be provided to assist in transferring a serverless application between computing platforms, such as by converting the portable serverless application to a container format. An interface may be provided that subscribes, on behalf of a container, to receive notifications of triggering events from a computing platform's notification service. The interface may provide a message to the container to indicate an occurrence of a triggering event, which may trigger execution of a serverless function by the container.

A system/process/computer program product for building multi-representational learning models for static analysis of source code includes receiving training data, wherein the training data includes a set of source code files for training a multi-representational learning (MRL) model for classifying malicious source code and benign source code based on a static analysis; generating a first feature vector based on a set of characters extracted from the set of source code files; generating a second feature vector based on a set of tokens extracted from the set of source code files; and performing an ensemble of the first feature vector and the second feature vector to form a target feature vector for classifying malicious source code and benign source code based on the static analysis.

A device may receive source code and identify, based on the source code, an abstract syntax tree representing an abstract syntactic structure of the source code. Based on the abstract syntax tree, the device may identify a closure, the closure implementing a function based on at least a portion of the abstract syntax tree. In addition, the device may perform an action based on the closure.

Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to perform operations processing, in an integrated development environment, a set of program code to identify an assertion within the set of program code; determining compile-time provability of a condition specified by the assertion; and presenting an error condition in response to failing to determine compile-time provability of the condition specified by the assertion, wherein determining compile-time provability of the condition specified by the assertion includes semantically converting the condition specified by the assertion into a Boolean, reducing the Boolean to an intermediate representation, and processing the intermediate representation to detect an expression within the intermediate representation that is non-constant at compile-time.