Abstract: Localizing a faulty microservice in a microservice architecture is achieved by developing healthy execution sequence data for comparison to execution sequences during system failures. Oftentimes the faulty microservice does not emit a failure signal. Frequent sub-sequences arising from log template time series data during healthy execution facilitates localization of faulty services when there is no failure signal from the faulty service.

Abstract: Systems, software, and methods for evaluating the scope of computer system changes related to automatic migration from one set of computing hardware to another provide methods and techniques that include evaluations for compliance with one or more policies prior to implementation, and then sequence and automate the migration tasks. A domain-specific language describes activity specifications and asset metadata, which is then used to generate interdependent activities in a project workstream on the basis of stored expert knowledge embedded in knowledge templates. Disaster recovery and “what-if” migration scenarios are tested in order to test and compare options of one or more proposed infrastructure changes.

Abstract: Systems/techniques that facilitate monolith-to-microservice refactoring via source-code-to-domain-model graph comparison are provided. In various embodiments, a system can access source code of a monolithic application and a target domain model corresponding to the monolithic application. In various aspects, the system can refactor the monolithic application into microservices, by aligning a first graph representing the source code with a second graph representing the target domain model.

Abstract: A process automatically generates configuration information used to build a target computing environment based on an image that depicts an architecture of the target computing environment. First, the process generates an original graph data structure having nodes and edges that respectively represent services and connectors detected in the image. The system then determines attributes of the target computing environment based on the original graph data structure. At least in part, the attributes describe a context in which the target computing environment is used, such as the type of industry associated with the target computing environment. The process then modifies the original graph data structure based on the attributes that have been determined, to produce a modified graph data structure. Finally, the process provides the configuration information to a deployment system, which uses the configuration information to deploy the target computing environment on a computing platform.

Abstract: A method may include: retrieving a plurality of code snippets from code repositories; generating a syntax representation, a property representation for each of the code snippets; receiving a query comprising a query code snippet, natural language keywords, and/or a string pattern; performing string-based matching and parser/syntax tree matching on the query and the tree representations, syntax matching on the query and the syntax representations, and property matching on the query and the property representations, wherein each of the matchings results in a score; combining the scores of the string-based matching, the parser/syntax tree matching, the syntax matching, and/or the property matching; identifying a plurality of code snippets of interest based on the combined scores; classifying the code snippets of interest using a machine learning classifier; outputting a list of the code snippets of interest with their classifications; and training the machine learning classifier based on user feedback.

Abstract: Systems and methods of scoring quality of open source software documentation are disclosed. In one aspect, a method includes downloading the open source software project code and documentation of the open source software project code, analyzing the open source software project code and the documentation, parsing the open source software project documentation into sections, validating the sections of the documentation with project or stack metrics, assessing a quality of sections of the documentation, assessing a quality of language of the documentation; and computing the quality score of the documentation based on the assessed quality of sections and assessed quality of language.

Abstract: A test-optimizing system for supporting shadow mode testing of ADS software included in an ADS-provided vehicle. One or more potential vehicle routes are retrieved. A geographical area covering the one or more potential vehicle routes is obtained and data of crucial locations associated with past vehicle situations identified as critical and/or challenging. Moreover, the test-optimizing system retrieves respective ODD for one or more candidate software respectively adapted to run in the background of the vehicle. ODD-compliant locations for respective candidate software are determined, by identifying locations out of the data of crucial locations lying within respective candidate software's ODD. At least a first test-compliant location along at least a first route out of the one or more potential vehicle routes is determined by identifying for at least a first candidate software, locations out of the ODD-compliant locations situated along the at least first route.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: This content development apparatus includes at least one storage medium and at least one processor. The storage medium is configured to store a plurality of resource data pertaining to content being created; and store a database pertaining to the resource data. The processor is configured to execute a plurality of editing processes; generate first information created for each type of the resource data and at least including a location of each of the resource data, and second information expressing an association between different types of the first information; store the first and the second information in the at least one storage medium; respond to a request from one of the editing processes to acquire, using a designated resource data, information indicating a different type of the resource data associated with the designated resource data; notify the editing process; and update the database.

Abstract: A method and an apparatus for issuing a smart contract are provided. The method may include: acquiring the smart contract; determining whether the smart contract comprises a preset function; generating, in response to determining that the smart contract does not include the preset function, verification data of the smart contract based on a proof-of-work technology; and sending a request for issuing the smart contract re based n the verification data.

Abstract: An image file obtaining method includes: determining a first value of each of N tasks in program source code, where the first value is first duration required for executing each of the N tasks or the first value is a ratio of the first duration to second duration, the second duration is duration required for loading each of the N tasks, and N is a positive integer; sorting the N tasks in descending order of the first values; and linking the sorted N tasks to obtain an image file. After downloading the image file, an embedded apparatus successively loads and executes the sorted N tasks, thereby shortening a startup time period of the embedded apparatus.

Abstract: A processor records a trace of execution of an execution entity, including recording initial processor register state into the trace. The recorded initial processor register state includes a value of at least one register of a plurality of registers. Subsequent to recording initial processor register state, and based on detection of a cache miss during execution of the execution entity, the processor records an influx of data imported into the processor cache in response to the cache miss. Also subsequent to recording initial processor register state, and based on execution of a non-deterministic processor instruction of the execution entity, the processor records at least one side effect of an access to at least one model-specific register, including recording a change in the value of the at least one register of the plurality of registers. The change results from the execution of the non-deterministic processor instruction of the execution entity.

Abstract: Techniques are described to automate deployment of an application as a cloud computing service. Such deployment may be accomplished on any underlying host infrastructure. As mentioned above, a typical application may engage in a number of build and deployment processes in order to run on a host computer with an infrastructure type. The techniques described herein may leverage an artificial intelligence computation library to identify which build and deployment processes are required by the application. Once identified, the user may select a host infrastructure type and create a container package with a runtime machine and components configure to run these build and deployment processes for that host infrastructure type. The container package may be used to generate an image from which the host computer can run the application as the cloud computing service. Other embodiments are described and claimed.

Abstract: A method and system determine a potential impact from software upgrades on a computing device. A device configuration is identified for a first computing device based on the software and hardware elements currently present. Other computing devices are identified in the network having installed the software application upgrade. A history of operating behavior associated with the software application upgrade is retrieved. The profile is analyzed for each of the other computing devices for conflicts with the software application. A determination is made as to whether the software application upgrade will potentially cause a failure in the first computing device based on the history of operating behavior associated with the software application upgrade and based on a state of similarity between the analyzed profile for each of the other computing devices and the profile of the first computing device. The end user is presented with a risk-based recommendation.

Abstract: Provided are a memory coupled compiling method and system of a reconfigurable chip. The memory coupled compiling method includes: acquiring a cycle number of a data flow graph (DFG); acquiring a linear transformation vector of the cycle number through a mapping time difference; determining whether a linear array of the linear transformation vector is acquired by a heuristic algorithm; acquiring a memory mapping result through a current DFG or acquiring a cycle number of the current DFG until the linear array is acquired, depending on the determination result.

Abstract: Methods and systems for transforming bytecodes using virtual artifacts are disclosed. In one aspect, a method is provided that includes receiving a build request to convert source code into a first bytecode. A first virtual artifact may be identified within the source code and it may be determined that a local repository does not store the first virtual artifact. A real artifact that corresponds to the first virtual artifact may be retrieved from a centralized repository. A bytecode transformation may be applied to the real artifact to generate a second bytecode and the second bytecode may be added to the first bytecode.

Abstract: Described herein are techniques that may be used to automate testing of services on mobile devices using visual analysis. In some embodiments, a machine learning model is trained using interaction data received from a number of mobile devices by correlating user selections with visual elements (e.g., icons). During execution of a testing routine on a mobile device, screenshots are obtained of a screen of the mobile device and provided to the machine learning model. An action is generated based on the provided screenshot that simulates a user action (e.g., a user touch on the screen of the mobile device) at a location of an icon or other visual element associated with the testing routine. These steps are repeated until an end-state of the testing routine is detected.

Abstract: A method for validating a software, particularly a driver-assistance software. The method includes receiving of a sensor signal that is to be processed by the software; determining of a reference signal by an adaptive filter based on the sensor signal, the reference signal representing an anticipated sensor signal; determining an error signal based on the sensor signal and the reference signal, the error signal representing a performance of the software; determining an anti-sensor signal by a machine-learning unit based on the sensor signal, the machine-learning unit being trained with sensor signals already evaluated; controlling of the adaptive filter by a control unit based on the determined error signal and the adaptive anti-sensor signal; and validation of the software based on the determined error signal.

Abstract: In some implementations, a device may receive extensibility data related to one or more custom code objects installed in a current environment. The device may classify the one or more custom code objects in one or more respective categories and determine one or more respective complexities associated with the one or more custom code objects based on the extensibility data. The device may generate an extensibility recommendation for deploying the one or more custom code objects to a target environment based on the one or more respective categories and the one or more respective complexities associated with the one or more custom code objects. The extensibility recommendation may be generated based on the one or more custom code objects satisfying extensibility conditions associated with the target environment. The device may provide an output relating to the extensibility recommendation.

Abstract: Embodiments for intelligent application scenario testing and error detection by a processor. One or more modified application scenarios may be automatically generated from an initial application scenario having configuration data and a plurality of operations relating to an error. The one or more modified application scenarios are variations of the initial application. The one or more modified application scenarios may be executed to detect the existence or non-existence of the error in the one or more modified application scenarios.