Abstract: A method of testing a change in a software code includes, searching a database of tests to identify a subset of the tests that include a function that executes the change, forming, from the subset, a multitude of groups each having a different execution path. The tests in the same group have the same execution path. The method further includes prioritizing the tests within each of the multitude of groups based on one or more testing characteristics, and selecting, from each of the groups, one or more of the prioritized tests to test the change.

Abstract: A technology management server for domain-based technology deployment and management is provided. The server includes a processor configured to receive an architecture definition file identifying a prior system status for each snapshotted application system. The processor is also configured to scan the application systems and classify each of the scanned application systems into an associated technology domain using a domain classification algorithm. The processor is configured to identify each scanned application system with a changed system status. The processor is configured to obtain a system update for each scanned application system with a changed system status, based on the technology domain. The system updates define implementation characteristics of each changed scanned application system. The processor is also configured to redefine the architecture definition file with the system updates.

Abstract: A data system is provided for analyzing and maintaining data obtained from one or more data sources on which the data system depends. The system includes a primary database including current values used by the system and a collection of executable algorithms used to generate the data maintained in the primary database. In response to receiving a notification regarding a change in one of the data sources, a dependency database is used to establish an execution order for algorithms of the algorithm collection that are directly or indirectly dependent on the changed data. The algorithms identified in the execution order are then executed in accordance with the execution order and the corresponding result is stored in the primary database. The system may include data harvesters adapted to recognize changes in the data sources and to generate and transmit corresponding change notifications when such changes occur.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-storage media, for prioritizing test cases. Processes may include obtaining test artifacts that were generated based on testing one or more legacy versions of a software application using multiple test cases, generating a risk index based at least on the test artifacts that were generated based on testing the one or more legacy versions of the software application using the multiple test cases, and training an ensemble model that is configured to identify likely unnecessary or redundant test cases in connection with in testing an updated version of the software application, based at least on the risk index.

Abstract: Techniques are provided for updating, managing, and maintaining a reference template for web service API testing. A plurality of logs, corresponding to a target application that provides one or more web services, may be captured. The captured logs may be filtered using information (e.g., URL extension) in the captured logs. Each filtered log may be parameterized and API request payload data may be extracted to generate a baseline template. A reference template may be compared with a corresponding baseline template to determine if they differ. If they differ, a test template may be generated by replacing each differing field of the reference template with a corresponding baseline template identifier. The test template may then be used in testing a web service API, such that a test template can conform with a changing web service API.

Abstract: A method, system and product for automated quality assessment of a programming task. Programming activity of a developer is monitored to obtain measurements of a plurality of metrics in a plurality of time segments. Functional correctness of the program at a last time segment of the plurality of time segments is determined. Based on the measurements of each of the metrics in the plurality of time segments, a plurality of features are computed. The plurality of features are indicative of a behavior of the developer while programming. A prediction model is utilized to provide an automated assessment based on the values of the plurality of features.

Abstract: A method of simulating codes that form a program configured to control a control target includes causing a processor to execute the following: first-setting a first process of executing a simulation of an operation of a code group excluding a specific code among the codes; second-setting a second process of executing a simulation of an operation of a specific model obtained by modeling the specific code; first-simulating, in the first process, a first simulation of a code preceding the specific code in the code group; second-simulating, in the second process, a second simulation of the specific model through use of an execution result of the first simulation by inter-process communication between the first process and the second process; and third-simulating, in the first process, a third simulation of a code succeeding the specific code through use of an execution result of the second simulation by the inter-process communication.

Abstract: An example method for visual testing and issue communication of programmed display of content includes obtaining a workflow of test scenarios for visual testing a display. The content displayed on the display is controlled by a set-top box (STB) device executing target instructions. The example method further includes identifying a visual testing issue associated with executing the test scenarios, and communicating the workflow and issue to a remote device for reproduction of the issue.

Abstract: A method of analyzing source code includes receiving, by a processor, an updated version of a computer program, the updated version including a source code. The method also includes preprocessing, by a compiler, the source code for a target computing platform. Preprocessing the source code by the compiler includes identifying a macro condition associated with one or more computer instructions enclosed by a macro, determining object code corresponding to the one or more computer instructions based on a current value of the macro condition, and generating object code and macro information for output to a debugger, the macro information including one or more breakpoint conditions in the macro.

Abstract: A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Abstract: A system to support validation and debugging of compiled low-level instructions for a machine learning (ML) network model on an ML-specific hardware. A compiler identifies well-defined boundaries in the ML network model based on primitives used to generate low-level instructions for the hardware. The ML network model is partitioned into units/layers/sub-graphs based on the plurality of well-defined boundaries. The compiler then generates an internal representation for each of the units wherein the internal representation is mapped to components in the hardware. Each of the units is compiled into a first set to be executed on the ML-specific hardware and a second set to be executed on a second computing device. The output results from executing the two sets of low-level instructions are compared to validate the first set of low-level instructions. If the outputs do not match fully, the first set of low-level instructions is debugged and recompiled.

Abstract: Disclosed are a system (100) and a method for an identification of web elements used in an automation test case. The system (100) and the method ensure reliable identification of an object despite dynamism. The system (100) and the method allow a tester to focus on the test case development of test cases rather than object management that in turn increases the success rate and efficiency of the automatic testing. The system (100) and the method track changes in object attributes, structure and behaviour thereby facilitating identification of dynamic objects automatically and correction of the object locator on the fly during test execution.

Abstract: Metric data may be received, where the metric data may include one or more time series, where each time series may include a set of metric datapoints corresponding to a set of time slices, and where each metric datapoint may be a measurement of a metric of a monitored object in an infrastructure. An infrastructure health score may be computed for each time slice in the set of time slices based on the metric data. A machine learning (ML) model may be trained based on metric datapoints corresponding to at least a current time slice and the infrastructure health score corresponding to a future time slice. The trained ML model may then be used to predict a future infrastructure health score based on at least current metric datapoints.

Abstract: Methods, apparatuses, and systems are described for artificial-intelligence based techniques for programmatically generating and integrating application programming interfaces (APIs). An example method may include, in response to receiving by one or more processors, an integration data object, processing, by the one or more processors, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object; programmatically generating, by the one or more processors, based at least in part on the one or more integration features, an application programming interface (API) model corresponding with the integration data object; and generating, by the one or more processors, an API generation data object corresponding with the API model for execution.

Abstract: A system for determining code ancestry. The system includes: a memory; and a processor communicatively coupled to the memory. The processor is configured to perform a method comprising: receiving a source code file; parsing a plurality of functions out of the source code file; generating fuzzy fingerprints from the plurality of functions; and storing the fuzzy fingerprints in a graph database.

Abstract: Systems and methods for testing components or scenarios with execution history are disclosed. A method may include: receiving, at a testing interface and from an application or program executed by a user electronic device, an identification of a test and one or more data layers of a plurality of data layers in pod to test, the plurality of data layers including a data collection layer, a data ingestion layer, a data messaging layer, a data enrichment layer, and a data connect layer; receiving, by the testing interface, a selection of testing parameters or values for the identified test; retrieving, by the testing interface, the identified test; executing, by the testing interface, the identified test on the identified one or more data layers using the selected testing parameters or values; retrieving, by testing interface, results of the execution of the test; and outputting, by the testing interface, the results.

Abstract: Automated query retry includes selecting a first node of a plurality of nodes of an execution platform in response to a query. The first node is configured with a first version of a database platform. A first execution of the query is performed using the first version of the database platform at the first node. The method further includes detecting the first execution results in a failed execution. A second execution of the query is scheduled. The second execution uses at least one of the first version of the database platform or a second version of the database platform configured at a second node of the plurality of nodes. The second version is released after the first version. A cause of the failed execution of the query at the first node is determined based at least in part on a result of the second execution of the query.

Abstract: A method includes decoding, by at least one hardware processor, a notification of a changed database code of a database. A query is executed responsive to the notification. The query indicates a data processing command and a data object in the database. A regression in the changed database code is detected based on multiple regression testing operations applied to the data processing command and the data object. Analysis of the regression is performed to detect a rollout parameter of a plurality of rollout parameters as a root cause of the regression. The plurality of rollout parameters are associated with the changed database code. A determination is made on whether to perform a mitigation action for the regression based on the rollout parameter.

Abstract: A center configured to communicate with an OTA master configured to control software update for electronic control units mounted on a vehicle includes one or more processors. The one or more processors are configured to store security information in which security levels required for the software update are associated with the electronic control units mounted on the vehicle, respectively, and generate a distribution package applied encryption at the security level associated with a target electronic control unit based on the security information. The target electronic control unit is an electronic control unit that is a target of the software update.

Abstract: Systems, computer program products, and methods are described herein for automated code resolution in an extended reality environment. The present invention allows a user (such as a software developer) to view source code discrepancies in real time using an extended reality (XR) environment. In this regard, the present invention focuses on electronic applications (and the electronic work products/electronic data hosted thereon) and represents a combined view of real-time applications and application requirements within an XR environment. A user may then visualize discrepancies between the current application and the application requirements via an XR platform (accessible using a virtual/augmented/mixed reality device) and proactively make edits, approvals, or otherwise interact with said application. The system may also be configured to automatically alter the source code to resolve said discrepancies.

Abstract: A method of generating a user interface for presentation to a user. The method comprises executing a first application computer program to provide a user interface, executing agent computer program code to interrogate and modify said user interface during execution of said first application computer program, and presenting said modified user interface. The first application computer program may be run on a server, while the modified user interface may be presented to a user at a client connected to said server.

Abstract: The disclosure describes techniques that enable detection of memory leaks of software executing on devices within a computer network. An example network device includes memory and processing circuitry. The processing circuitry monitors a usage of the memory by a software component operating within the network device. The processing circuitry periodically determines a memory growth pattern score for the software component based on the usage of the memory. The processing circuitry also predicts whether the user-level process is experiencing a memory leak based on the memory growth pattern score. The processing circuitry applies confirmation criteria to current memory usage of the software component to confirm that the software component is experiencing the memory leak. When the software component is experiencing the memory leak, the processing circuitry generates an alert.

Abstract: Processor trace systems and methods are described. For example, one embodiment comprises executing instrumented code by a compiler, the instrumented code including at least one call to un-instrumented code. The compiler can determine the at least one call to un-instrumented code is a next call to be executed. A resume tracing instruction can be inserted into the instrumented code prior to the at least one call to the un-instrumented code. The resume tracing instruction can be executed to selectively add processor tracing to the at least one call to the un-instrumented code, and the at least one call to the un-instrumented code can be executed.

Abstract: The disclosed technology teaches rejecting, during validation, a sequence of components intended for interacting with a user. Included are providing a sequence setup GUI supporting construction of an executable sequence by connecting at least five components in a directed graph, and tracing multiple paths through the directed graph, including from at least one conditional branch at a first up-chain component, in which down-chain components accept as input and depend on output from at least one up-chain component, referred to as input chain dependencies. Also included are locating at least one error in use of a particular down-chain component when invoked following one of the multiple paths, where the error results from failure to satisfy any of the input chain dependencies of the particular down-chain component, and reporting the error during validation to a user of the GUI, before passing the sequence of components from validation to use in production.

Abstract: Various embodiments are directed to performing automated software testing configuration management operations. In one embodiment, a method is provided. The method includes generating a graph data structure including nodes representing test steps across test cases of a software testing data object for a software application platform and including segments each connecting two nodes and representing test cases having the two represented test steps. The method further includes receiving a query describing a testing change and generating extracted features from the query. The method further includes generating test outcome requirements and graph portions of the graph data structure relevant to the testing change using supervised machine learning models and the extracted features.

Abstract: An embodiment may involve storage containing incident logs and mappings between incident logs and vector representations generated by a machine learning (ML) model.

Abstract: Embodiments of the present disclosure relate to a method, system and computer program product for semantic search based on a graph database. In some embodiments, a method is disclosed. According to the method, the user jobs of a user are obtained from a first software product. Based on the user jobs, target test cases are selected from a plurality of test cases associated with the first software product and a second software product. The target test cases are applied to the first software product and the second software product, and in accordance with a determination that a result of applying the target test cases satisfies a predetermined criterion, an instruction is provided to indicate migrating from the first software product to the second software product. In other embodiments, a system and a computer program product are disclosed.

Abstract: A system and a method for performing a test of an application using an automation bot are provided. The method comprises accessing the application to be tested. The method comprises executing the test of the application using the automation bot. The automation bot is configured to interact with one or more other applications. The one or more other applications are different from the application. The method comprises determining one or more test results of the application based on the execution of the test. Further, the method comprises generating a notification indicative of the determined one or more test results.

Abstract: There are provided systems and methods for a data integration framework that provides an institutional or organizational user data enrichment capability locally. Specifically, instead of relying on the fraud detection platform to constantly updating and/or building new data connectors to intake data from updated or a new data provider, an institutional user, such as a financial institution, may receive a software development kit (SDK) from the fraud detection platform, using which the institutional user may build its own data connector deployed at the institutional user.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate generation of microservices from a monolithic application based on runtime traces are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that learns cluster assignments of classes in a monolithic application based on runtime traces of executed test cases. The computer executable components can further comprise a cluster component that employs the model component to generate clusters of the classes based on the cluster assignments to identify one or more microservices of the monolithic application.

Abstract: In one example, a cloud-based services exchange comprises a plurality of interconnection assets configured to connect a customer of the cloud-based services exchange to one or more cloud service providers, the plurality of interconnection assets including a virtual circuit by which the customer accesses a cloud service from the one or more cloud service providers; and an orchestration engine configured to modify the plurality of interconnection assets.

Abstract: Methods and devices for an automated Internet of Things (IoT) certification service to verify plug and play capabilities of one or more IoT devices for use with a cloud service provider may include receiving a device identification of an IoT device to certify with the cloud service provider. The methods and devices may include receiving a selection of one or more certification tests to perform to plug and play capabilities of the IoT device. The methods and devices may include performing the one or more certification tests. The methods and devices may include generating a device certification for the plug and play capabilities of the IoT device in response to the one or more certification tests passing.

Abstract: A system, method, and computer-readable medium are disclosed for predicting a defect within a computer program comprising: accessing a code base of the computer program, the code base of the computer program comprising a plurality of computer program files; training the defect prediction system, the training including performing a historical analysis of defect occurrence patterns in the code base of the computer program; analyzing a commit of the computer program to identify a likelihood of defect occurrence within each of the plurality of files of the computer program; and, calculating a defect prediction metric for each of the plurality of files of the computer program, the defect prediction metric providing an objective measure of defect prediction for each of the plurality of files of the computer program.

Abstract: Systems, apparatuses and methods may provide for developer stage technology that embeds binary code into an application binary file, wherein the binary code corresponds to vector functions and non-vector functions in statically typed source code, and generates intermediate representation (IR) data, wherein the intermediate representation data corresponds to the vector functions in the statically typed source code. Additionally, the developer stage technology embeds the IR data in the application binary file. Moreover, deployment stage technology may generate a first compilation output based on the application binary file and detect a capability change in an execution environment associated with the first compilation output. The deployment stage technology may also generate, in response to the detected capability change, a second compilation output based on the first compilation output.

Abstract: A method for operating a cooling system of a transformer, wherein the transformer is cooled via a cooling liquid that circulates in the cooling system that includes a heat-exchanger, devices for increasing heat exchange performance of the at heat-exchanger and a controller, where in a normal operating state, the controller adjusts power of the devices for increasing the heat exchange performance as a function of a measured upper temperature and where, irrespective of the measured upper temperature, the controller refrains from activating the devices and/or operates the devices at a reduced power relative to the normal operating state if the lower temperature of the cooling liquid lies below a lower threshold value during operation of the transformer to achieve improved characteristics of the cooling system during operation under low environmental temperatures of the transformer, particularly in the case of a turn-on operation following lengthy storage in a cold state.

Abstract: Methods and apparatuses are described for automated testing of mobile devices. A server establishes a connection with a client device via a high-latency connection. The server receives input from the client device comprising (i) a selection of mobile devices for testing and (ii) a selection of a test interaction. The server generates predicted interactions for each of the selected mobile devices using historical device interaction data. The server converts the predicted interactions into test scripts, each test script formatted for use with one of the selected mobile devices. The server deploys each test script for execution on the corresponding mobile device, and transmits results from the test script executions to the client device.

Abstract: A system for secure identity data tokenization and processing, the system adapted to receive a loan provisioning request from a merchant computing device associated with an individual and to receive, from a secure identity verification computing device, a secure identity token data object attesting to an identity of the individual. A secure identity token data object is processed to verify the identity of the individual and to initiate a loan origination process. A request for an electronic transaction to be paid or partially paid using funds represented in the dynamic card token data object associated with the unique loan identifier data value and a a payment package data object is generated to encapsulate a tokenized representation of a dynamic card data object associated with the dynamic card token data object, an electronic representation of the funds to be provided from the dynamic card token data object.

Abstract: A pair of virtualized security device initialization data sets are received at a first virtualization server from respective sources prior to a launch of a compute instance at the server. A first virtualized security device is initialized using the data sets, and used to generate cryptographic artifacts used by the compute instance. A data item which was included in one of the data sets is modified after the cryptographic artifacts are generated. Additional cryptographic artifacts are generated by a second virtualized security device at a second virtualization server to which the compute instance has been migrated. The second virtualized security device is initialized using at least the modified data item.

Abstract: Device configurations for a machine-learned model. A device analytics system includes an electronic processor configured to determine, for a machine-learned model configured to detect a plurality of features, a prioritization ranking for a plurality of input parameters provided to the machine-learned model and receive, for each device, a confidence value for each feature included in the plurality of features. The electronic processor is configured to determine, for each device included in the plurality of devices, a performance value for each feature included in the plurality of features based on the prioritization ranking for the plurality of input parameters and the confidence value for each feature for the respective device. The electronic processor is configured to select, based on the performance value for each device included in the plurality of devices for a feature, a device configuration for the machine-learned model, and implement the selected device configuration.

Abstract: Dynamically generating data tests includes: receiving test templates and metadata for a plurality of data objects and, on a trigger condition: based on at least the metadata and the test templates, determining a current set of templated data tests, wherein determining the current set of templated data tests comprises: determining at least one templated data test, from a prior set of templated data tests, to cease using; determining at least one templated data test to add to the current set of templated data tests; and determining, within the current set of templated data tests, at least one templated data tests to regenerate; generating the at least one templated data test to add to the current set of templated data tests; regenerating the least one templated data test to regenerate; and executing templated data tests, within the set of current templated data tests, on the plurality of data objects.

Abstract: An experimentation platform controls testing of features by an application server. Based on a user identifier, the experimentation platform determines which feature should be provided, and the application server provides the corresponding version of a user interface. If the user behavior data shows that using a tested feature results in an improvement, the tested feature will be adopted. To determine whether or not an improvement is observed, a statistically significant amount of data is gathered. The experimentation platform gathers data regarding user behavior for the feature versions and, in response, adjusts the frequency at which each version is served. Providing the proposed version to an increased percentage of users decreases the total number of page serves required to gather statistically significant data. The experimentation platform may provide an updated projected time to completion of testing based on the changed percentage of users receiving the proposed version.

Abstract: An authentication system comprises a browser extension and a password manager application. The browser extension can be configured for execution on a first user device. The browser extension can be configured to display a response code and receive a login credential from a server. The response code can comprise a unique session identifier identifying the browser extension and a user browsing session. The password manager application can be configured for execution on a second user device. The second user device can have a scanner configured to scan the response code. The password manager application can be configured to extract the unique session identifier, parse the unique session identifier into session identifier content, send a portion of the session identifier content to the server, receive an approval from a user of the second user device, and send a notification to the server.

Abstract: A data processing apparatus is provided that includes storage circuitry to store a plurality of interconnected instructions. Analysis circuitry analyses the instructions to determine a degree of uniqueness of profile measurements of a control flow path fragments within the instructions.

Abstract: Systems and method for URL filtering are provided herein. In some embodiments, a system includes a processor programmed to receive a URL request to access a resource associated with the URL; perform a first layer of URL filtering by comparing the URL to a blocklist of malicious URLs; determine that the URL does not match a URL on the blocklist; perform a second layer of filtering by applying a machine learning algorithm to analyze the URL to predict whether the URL is malicious; and generate and transmit a URL filter determination that the URL is malicious and update the blocklist to include the URL.

Abstract: Embodiments facilitate testing of software functions, by elaborating upon a small volume test sample originally provided by a developer. Where the original test sample is determined to be numerical, embodiments perform repetition techniques to generate corpuses of test data of varying scope representing operation over different timescales. For original test samples of greater complexity (e.g., including text and/or hierarchical output states of the software function), embodiments select a relevant Machine Learning (ML) model and apply that ML model to generate the corpuses of test data. These are then available for loading to measure one or more performance parameters (e.g., runtime, memory consumption; processing consumption; bandwidth) of the software function. Broadly reported non-linear behavior of performance parameter(s) may reveal lack of suitability of the generated data corpuses for testing. More specific non-linear behavior may indicate failure of the software function to pass certain tests.

Abstract: A trace-data preparation circuit including a filtering circuit to receive traced memory-write data and a First In First Out buffer coupled with the filtering circuit to receive selected memory-write data filtered by the filtering circuit. The trace-data preparation circuit further including a data compression circuit to provide packaging data to a packaging circuit that groups the selected memory-write data.

Abstract: An apparatus comprises a processing device configured to obtain testing logs generated by executing test cases on information technology assets of an information technology infrastructure, to parse the testing logs to generate a set of log event templates for testing actions performed during execution of the test cases on the information technology assets of the information technology infrastructure, to generate vector representations of the test cases utilizing the generated set of log event templates, and to perform, utilizing one or more machine learning-based hierarchical clustering algorithms that take as input the generated vector representations of the test cases, hierarchical clustering of the plurality of test cases. The processing device is also configured to generate, based at least in part on the hierarchical clustering results, testing plans for a given information technology asset of the information technology infrastructure.

Abstract: An electronic product testing system includes: a testing instruction generator configured to provide a user interface for generating an electronic file, the electronic file containing product testing instruction; and a testing device having a processing unit configured to execute the electronic file to perform testing of a product based on the product testing instruction in the electronic file; wherein the processing unit is configured to perform testing of the product by simulating human actions based on the product testing instruction in the electronic file.

Abstract: Household appliances and methods of testing are provided. The household appliance includes a mechanical component and a controller in operative communication with the mechanical component. The controller has a partitioned memory. The partitioned memory includes a testing partition and a control partition. The controller is configured for and/or the method includes authorizing a user of the household appliance and verifying a personality of the a household appliance. Testing software is loaded into the testing partition of the memory of the controller. The testing software is executed from the testing partition, whereby the mechanical component of the household appliance is operated. The method also includes and/or the controller is also configured for exiting remote testing mode after executing the testing software and deleting the testing software from the partitioned memory when exiting the remote testing mode.

Abstract: In some implementations, a regression prediction platform may obtain one or more feature sets related to an impending code change, wherein the one or more feature sets may include one or more features related to historical code quality for a developer associated with the impending code change or a quality of a development session associated with the impending code change. The regression prediction platform may provide the one or more feature sets to a machine learning model trained to predict a risk associated with deploying the impending code change based on a probability that deploying the impending code change will cause breakage after deployment and/or a probability that the impending code change will cause support issues after deployment. The regression prediction platform may generate one or more recommended actions related to the impending code change based on the risk associated with deploying the impending code change.