Abstract: This disclosure relates generally to a system and method for auto-generation of test specifications from internet of things (IoT) solution specifications of IoT-enabled components of an IoT network. Testing is the complementary and most important part of any IoT network. Herein, a domain specific language (DSL) is used to specify capability of IoT enabled components. IoT solution specifications are captured from capabilities of IoT enabled components using a predefined activity DSL. A flow of activity is captured to assert transitions among one or more activities based on guard conditions. The flow of activity is analyzed to generate test specifications automatically using a Test Specification DSL based on the asserted transitions. The test specifications are implemented automatically in a predefined target language corresponding to the IoT enabled components.

Abstract: One or more execution traces of an application are accessed. The one or more execution traces have been collected at a basic block level. Basic blocks in the one or more execution traces are scored. Scores for the basic blocks represent benefits of performing binary slimming at the corresponding basic blocks. Runtime binary slimming is performed of the application based on the scores of the basic blocks.

Abstract: A polygon simplification computer system for performing polygon simplification within a web application pipeline has a memory device including a three-dimensional data model (3D data model), a Node.js environment, and a polygon simplification module for performing one or more polygon simplification operations on the 3D data model. The polygon simplification module has a binding component including a set of bindings that convert JavaScript into C++ source code, and a mesh simplification module comprising a C++ compiled polygon simplification framework. The polygon simplification module receives a request for the 3D data model, converts the request into a set of C++ instructions, transmits the instructions to the mesh simplification module, receives a 3D simplified data model, and transmits the 3D simplified data model to a client computing device.

Abstract: Disclosed herein is a software technology for facilitating an interactive conversational session between a user (e.g., a client, a patient, etc.) and a digital conversational character. For instance, in one aspect, the disclosed process may involve two primary phases: (1) an authoring phase that involves a first user accessing a content authoring tool to create a given type of visual conversation application that facilitates interactions between a second user and a digital conversational character in an interactive conversational session, and (2) a rendering phase that involves the second user accessing the created visual conversation application to interact with the digital conversational character in an interactive conversational session.

Abstract: Systems and methods are described for graphical user interfaces that enable users to graphically design nodes and interconnections of pipelines for preprocessing data later ingested into an indexing system. The preprocessing can include receiving messages published to a first publish-subscribe messaging system, the messages containing raw machine data generated by one or more components in an information technology environment, performing one or more processing operations on at least some of the messages to generate preprocessed messages, republishing the preprocessed messages to a second publish-subscribe messaging system, and providing to the indexing system, a subset of the messages from the second publish-subscribe messaging system. Nodes in the pipeline can specify the preprocessing operations, and interconnections in the pipeline can represent data flow through the nodes of the pipeline.

Abstract: A method, a computer system, and a computer program product for parallel conversion is provided. Embodiments of the present invention may include analyzing raw classical code using a code embedded deep learning model. Embodiments of the present invention may include analyzing running classical code using a deep learning model. Embodiments of the present invention may include marking a location of the raw classical code for a first quantum conversion. Embodiments of the present invention may include suggesting a memory size of the running classical code for a second quantum conversion. Embodiments of the present invention may include aggregating the raw classical code for the first quantum conversion. Embodiments of the present invention may include aggregating the running classical code for the second quantum conversion.

Abstract: The subject technology receives a neural network model in a model format, the model format including information for a set of layers of the neural network model, each layer of the set of layers including a set of respective operations. The subject technology generates neural network (NN) code from the neural network model, the NN code being in a programming language distinct from the model format, and the NN code comprising a respective memory allocation for each respective layer of the set of layers of the neural network model, where the generating comprises determining the respective memory allocation for each respective layer based at least in part on a resource constraint of a target device. The subject technology compiles the NN code into a binary format. The subject technology generates a package for deploying the compiled NN code on the target device.

Abstract: Methods, systems, and computer program products for automated breakpoint creation using machine learning are provided. Aspects include obtaining a bug report for a software and source code for the software and analyzing the bug report to determine a bug type for the bug report, where analyzing the bug report includes using a bug type labeling model. Aspects also include analyzing the source code to identify a code snippet in the source code based on the bug type, where analyzing the source code includes using a source code detection model. Aspects further include inserting a breakpoint in the source code at the code snippet.

Abstract: Implementations are described herein for generating embeddings of source code using both the language and graph domains, and leveraging combinations of these semantically-rich and structurally-informative embeddings for various purposes. In various implementations, tokens of a source code snippet may be applied as input across a sequence-processing machine learning model to generate a plurality of token embeddings. A graph may also be generated based on the source code snippet. A joint representation may be generated based on the graph and the incorporated token embeddings. The joint representation generated from the source code snippet may be compared to one or more other joint representations generated from one or more other source code snippets to make a determination about the source code snippet.

Abstract: A benchmark generating system is presented. The system monitors an execution of a computer program to collect performance traces for one or more load metrics. The system generates a program model based on the collected performance traces. Each state of the program model corresponds to a workload level at one load metric or a combination of workload levels at the two or more load metrics. The system also generates one or more workload models based on the collected performance traces. Each workload model is configured to generate simulated workload for one load metric of the one or more load metrics at a workload level that is determined based on a state of the program model. The system provides the generated program model and the generated workload models as a benchmark of the computer program.

Abstract: A class unloading method comprises: loading, by an electronic device, n classes after an application is started, where n is a positive integer; generating a reference mapping table, where the reference mapping table includes a reference relationship between the n classes and m class objects corresponding to the n classes and a dependency relationship between the m class objects corresponding to the n classes, the dependency relationship is used to represent an interdependency mapping relationship between different class objects, and m is a positive integer greater than or equal to n; and unloading a first class of the n classes based on the reference mapping table in an operation process of the application.

Abstract: A device which runs software applications includes a network interface, a non-transitory computer readable storage medium and at least one processor. The device identifies that a link for installation of a new software application is selected by user interaction with a software application that is running on the device. In response to the identification, an installation client is invoked to run in the background on the device without exiting the currently-running software application. The installation client is instructed to automatically download an installation file of the new software application over the network using the network interface. The new software application is installed on the device using the downloaded installation file.

Abstract: The objective of the present invention is to prevent a conflict between variable names and consequently the unintentional overwriting of data when a plurality of programs that define a shared variable exist. A control device (1) is equipped with a program management part (11), a data storage part (13a), and a shared variable symbol table (13b). The program management part (11) acquires an identifier for a first user program, generates a shared variable name that includes the identifier and a variable name of a shared variable defined in the first user program, associates the shared variable name and an address of the shared variable with each other, and records these in the shared variable symbol table (13b).

Abstract: An apparatus is configured to create a logging template comprising instructions for managing logging of scripts executed from a front-end and implemented in a back-end of an information technology workflow orchestration system. The apparatus is also configured to provide the logging template in a catalog of application programming interfaces in a user interface of the front-end enabling selection of instructions from the logging template for inclusion in scripts generated in the front-end. The processing device is further configured to generate a given script by selecting, utilizing the user interface, a set of elements from the catalog including one or more instructions from the logging template. The processing device is further configured to execute the given script in the front-end utilizing at least one of the instructions from the logging template to view, in the user interface of the front-end, logs produced by the back-end in response to the given script.

Abstract: Methods of accelerating the execution of neural networks are disclosed. A description of a neural network may be received. A plurality of operators may be identified based on the description of the neural network. A plurality of symbolic models associated with the plurality of operators may be generated. For each symbolic model, a nested loop associated with an operator may be identified, a loop order may be defined, and a set of data dependencies may be defined. A set of inter-operator dependencies may be extracted based on the description of the neural network. The plurality of symbolic models and the set of inter-operator dependencies may be analyzed to identify a combinable pair of nested loops. The combinable pair of nested loops may be combined to form a combined nested loop.

Abstract: An interrelated set of tools and methods is disclosed for: (1) measuring the relationship between software source code attributes (such as code quality, design quality, test quality, and complexity metrics) and software economics outcome metrics (such as maintainability, agility, and cost) experienced by development and maintenance organizations, (2) using this information to project or estimate the level of technical debt in a software codebase, (3) using this information to estimate the financial value of efforts focused on improving the codebase (such as rewriting or refactoring), and (4) using this information to help manage a software development effort over its lifetime so as to improve software economics, business outcomes, and technical debt while doing so.

Abstract: Source code is generated from one or a plurality of blocks of a block diagram. The block diagram is hierarchical and includes at least a first subsystem that is configured for an incremental code generation. The block diagram references a definition database which includes a first object. The method for generating the source code includes: opening, by a computer system, the block diagram including the first subsystem in a model editor; generating, by the computer system, source code for the first subsystem, wherein generating the source code for the first subsystem includes determining information about the first subsystem; storing, by the computer system, the information about the first subsystem in the first object; and generating, by the computer system, source code for the block diagram, wherein the first object is read out to influence at least one parameter for generating the source code for the block diagram.

Abstract: The present disclosure provides a method, apparatus, device and computer-readable storage medium for guiding symbolic execution. According to embodiments of the present disclosure, it is possible to determine the specific code region of the program, and obtain the program loop output of the program corresponding to the specific code region of the program by using the program inverse analysis method, so that it is possible to obtain the program loop input of the program corresponding to the specific code region by using the program loop predictor according to the program loop output of the program. In this way, the obtained program loop input of the program corresponding to the specific code region may be used to guide the symbolic execution to filter out impossible execution paths and jump out of the program code and reach the specific code region, thereby improving the reliability of the symbolic execution.

Abstract: A computer-implemented method, system and computer program product for identifying implicit dependencies between code artifacts. Co-defect association rules between code artifacts are generated, where such co-defect association rules include a prediction of how likely there will be a defect in a code artifact when there is a defect in an associated code artifact. After detecting a defect in a first code artifact, the co-defect association rules are reviewed to identify any code artifacts associated with the first code artifact. If there is a code artifact associated with the first code artifact, and if the probability of the associated code artifact being defected when the first code artifact is defected exceeds a threshold value, then a recommendation is made to the user to review not only the first code artifact that was defected but also its associated code artifact for a potential defect.

Abstract: An apparatus includes a memory and a processor configured to merge a first code branch and a second code branch of a code development project. The memory stores a machine learning model. The processor identifies a first conflict and a second conflict between the first code branch and the second code branch. The processor uses a machine learning model to determine a resolution for the first conflict based in part on a set of requirements for the code development project. The processor then applies the resolution to the integrated code. After resolving the first conflict, the processor uses the machine learning model to determine a resolution for the second conflict based in part on the set of requirements for the code development project and the resolution for the first conflict. The processor then applies the resolution for the second conflict to the integrated code.