Abstract: An issue tracking system (ITS) is disclosed. The ITS comprises a user interface configured to receive user input defining one or more issues and an interface module configured to interface with a source code management system (SCM) and receive from the SCM, data pertaining to linked SCM-repository events. The received data is processed to generate an order for the SCM-repository events to which the issue is linked. A display module is configured to concurrently display a plurality of issues, each issue being displayed with issue information and issues having linked SCM-repository events being displayed with linked SCM-repository event information, said linked SCM-repository event information being displayed according to the generated order.

Abstract: Disclosed are a general-purpose machine learning model generation method and apparatus, and a computer device and a storage medium. The method comprises: acquiring task parameters of a machine learning task (S1201), performing classification processing on the task parameters to obtain task instructions and model parameters (S1202), aggregating the task instructions and the model parameters according to a data type to obtain stack data and heap data (S1203), and integrating the stack data and the heap data to obtain a general-purpose machine learning model (S1204). By means of the method, compiled results of a corresponding general-purpose model in the running of an algorithm can be directly executed, which avoids repetitive compilation, thus greatly improving the efficiency of machine learning algorithm implementation and shortening the time from compilation to obtaining execution results.

Abstract: Systems, methods, and apparatuses are described for analyzing differences in program dependencies, such as libraries. Code of a computer program may be dependent on a first version of a library. The first version of the library may comprise one or more first functions. Based on the first version of the library, a tree model representing the first version of the library and corresponding functions that the library comprises may be generated. A second version of the library may be determined. The one or more first functions of the first version of the library may be compared to one or more second functions of the second version of the library. The differences may be output by, e.g., displaying the differences using a modified tree model based on the generated tree model, and/or code of the program may be updated.

Abstract: Techniques for generating rules from documentation are described. For example, a method for generating rules may include generating one or more rules from documentation by: extracting a plurality of chunks from the documentation, inferring one or more candidate rules from the extracted chunks, mining the inferred one or more candidate rules to determine at least one of the one or more candidate rules is to be included in rule generation, classifying the at least one mined one or more candidate rules as one or more rules, and extracting information to generate the one or more rules.

Abstract: Methods and systems for avoiding or deferring data copies are disclosed. Using a virtual machine, it is determined whether a set of program code comprises references to a data object after an operation to generate a copy of the data object. If not, a set of optimized program code is generated in which the operation to copy the data object is replaced with an operation to update a reference. Using the virtual machine, it is determined whether the set of program code comprises an operation to generate a copy of a buffer object. If so, a set of further optimized program code is generated, comprising an allocation of one or more memory pages to store the buffer object with a copy-on-write parameter instead of the operation to generate the copy of the buffer object.

Abstract: An API latency estimation system estimates latencies as a function of subcomponent parameters. The system may obtain first information indicative of at least a characteristic of data of a request provided to an API and second information indicative of at least a utilization of a first subcomponent of the API used to fulfill a subtask of a task of the request. An estimated latency for the first subcomponent to fulfill the subtask is determined at least in part by applying a latency estimation model for the API to at least the first information and the second information. If a comparison of the estimated latency to a measured latency for the first subcomponent to perform the subtask indicates a potential anomaly, then an indication of the potential anomaly may be outputted. The model may be updated with API request fulfillment data that is not anomalous.

Abstract: Disclosed are a general machine learning model generation method and apparatus, and a computer device and a storage medium. The method comprises: acquiring task parameters of a machine learning task (S1201); performing classification processing on the task parameters to obtain task instructions and model parameters (S1202); aggregating the task instructions and the model parameters according to a data type to obtain stack data and heap data (S1203); and integrating the stack data and the heap data to obtain a general machine learning model (S1204). By means of the method, compiled results of a corresponding general model in the running of an algorithm can be directly executed, which avoids repetitive compilation, thus greatly improving the efficiency of machine learning algorithm implementation and shortening the time from compilation to obtaining execution results.

Abstract: Disclosed are a general machine learning model generation method and apparatus, and a computer device and a storage medium. The method comprises: acquiring task parameters of a machine learning task (S1201); performing classification processing on the task parameters to obtain task instructions and model parameters (S1202); aggregating the task instructions and the model parameters according to a data type to obtain stack data and heap data (S1203); and integrating the stack data and the heap data to obtain a general machine learning model (S1204). By means of the method, compiled results of a corresponding general model in the running of an algorithm can be directly executed, which avoids repetitive compilation, thus greatly improving the efficiency of machine learning algorithm implementation and shortening the time from compilation to obtaining execution results.

Abstract: Techniques for generating user interfaces (UIs) for field devices on a host device are described. A field device driver installed on the host device transmits a request for UI configuration to a field device. The field device includes a plurality of sets of UI parameters associated with configuration of the UI. The field device is configured to select a set of UI parameters from the plurality of sets of UI parameters based on application information provided to the field device. The field device driver receives the set of UI parameters from the field device in response to the request. Based on the set of UI parameters, the host device configures and generates the UI.

Abstract: Systems and methods may aggregate and organize implicit and explicit initialization, reset, and termination operations defined throughout the hierarchy of an executable. The systems and methods may analyze the model and identify implicit and explicit initialization, reset, and termination operations defined at various hierarchical levels. The systems and methods may aggregate the implicit and explicit initialization, reset, and termination operations into an initialize callable unit, a reset callable unit, and a termination callable unit. The systems and methods may apply optimizations to the callable units, and resolve conflicts. The systems and methods may define a single entry point for each of the initialize, reset, and termination callable units.

Abstract: Disclosed embodiments relate to a method and device for optimizing compilation of source code. The proposed method receives a first intermediate representation code of a source code and analyses each basic block instruction of the plurality of basic block instructions contained in the first intermediate representation code for blockification. In order to blockify the identical instructions, the one or more groups of basic block instructions are assessed for eligibility of blockification. Upon determining as eligible, the group of basic block instructions are blockified using one of one dimensional SIMD vectorization and two-dimensional SIMD vectorization. The method further generates a second intermediate representation of the source code which is translated to executable target code with more efficient processing capacity.

Abstract: Disclosed are a general machine learning model generation method and apparatus, and a computer device and a storage medium. The method comprises: acquiring task parameters of a machine learning task (S1201); performing classification processing on the task parameters to obtain task instructions and model parameters (S1202); aggregating the task instructions and the model parameters according to a data type to obtain stack data and heap data (S1203); and integrating the stack data and the heap data to obtain a general machine learning model (S1204). By means of the method, compiled results of a corresponding general model in the running of an algorithm can be directly executed, which avoids repetitive compilation, thus greatly improving the efficiency of machine learning algorithm implementation and shortening the time from compilation to obtaining execution results.

Abstract: According to one or more embodiments of the present invention, a computer-implemented method for machine code analysis includes executing a set of test cases associated with a software product. The method further includes determining a failing test case, from the set of test cases. The method further includes identifying a portion of a machine code of the software product, the portion of the machine code corresponding to the failing test case. The method further includes analyzing the portion of the machine code to identify a pattern of machine code causing the failing test case to fail. The method further includes searching the machine code of the software product to find the identified pattern of machine code.

Abstract: Implementations generally relate to providing process modes. In some implementations, a method includes receiving view descriptors at a client device, where the view descriptors define how a process model is rendered and define how the process model behaves when rendered. The method further includes storing the view descriptors at the client device. The method further includes receiving, at the client device, a process mode selection from a user, where the process mode selection selects a process mode of a plurality of process modes, and where the selected process mode is associated with a set of the view descriptors. The method further includes retrieving the process model from a server. The method further includes applying the process model at the client device based on the set of the view descriptors associated with the selected process mode.

Abstract: A microcode update system includes at least one memory device having a code region and a data region, and a microcode update engine that receives a microcode update, and writes the microcode update to the data region of the at least one memory device. Subsequent to writing the microcode update to the data region of the at least one memory device, the microcode update engine utilizes initialization code in the code region of the at least one memory device to perform initialization operations. During a microcode update portion of the initialization operations, the microcode update engine identifies the microcode update in the data region of the at least one memory device, and performs microcode update operations using the microcode update in the data region of the at least one memory device.

Abstract: A device receives historical application creation data that includes data associated with creation of a plurality of applications, and processes the historical application creation data, with one or more data processing techniques, to generate processed historical application creation data. The device trains a machine learning model, with the processed historical application creation data, to generate a trained machine learning model, and receives new application data associated with a new application to be created. The device processes the new application data, with the trained machine learning model, to generate one or more predictions associated with the new application, and performs one or more actions based on the one or more predictions associated with the new application.

Abstract: A system for generating simulated LiDAR data may include a depth offset generator and an intensity value generator. The depth offset generator may be configured to receive environment data including depth information, e.g., a depth map, the environment. The depth offset generator may determine an optical flow field from the depth information and estimate depths for positions to simulate LiDAR sensor data. field. The depth offset generator can also generate timestamp information based on attributes of the simulated LiDAR sensor, and determine the estimated depths using the timestamp information. The intensity value generator may be configured to determine an intensity for pixels based on physical attributes associated with those pixels. The simulated LiDAR data may be used in simulations run on autonomous vehicle control systems.

Abstract: A microservice and the recipe for that microservice are automatically created by modifying a local environment in an integrated development environment executing on a computing system to construct a desired microservice, recording commands entered while modifying the local environment, computing a list of changes from the recorded commands that change the local environment and compiling the list of changes into a recipe comprising commands and dependencies sufficient to assemble an operating system and software files that are sufficient to instantiate the desired microservice.

Abstract: Disclosed herein are techniques for visualizing and configuring controller function sequences.

Abstract: A hardware acceleration method includes: obtaining compilation policy information and a source code, where the compilation policy information indicates that a first code type matches a first processor and a second code type matches a second processor, analyzing a code segment in the source code according to the compilation policy information, determining a first code segment belonging to the first code type or a second code segment belonging to the second code type, compiling the first code segment into a first executable code, sending the first executable code to the first processor, compiling the second code segment into a second executable code, and sending the second executable code to the second processor.