Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Abstract: The disclosure provides a data transmission device. The data transmission device includes a storage medium, a processor and a communication interface. The storage medium is configured to store a plurality of applications. The processor is coupled to the storage media and configured to determine a host application among the plurality of applications, where the host application includes a software development kit (SDK) program, and executes the SDK program of the host application to request behavior data of the plurality of applications. The communication interface is coupled to the processor and is configured to transmit the behavior data to a server.

Abstract: A dynamic configuration trace capture technique enables software developers to monitor, diagnose and solve errors associated with application development and production. A client library of an investigative platform is loaded in a user application and interacts with an agent process to instrument executable code of the user application. A dynamic configuration specifies information, such as methods and associated arguments, variables and data (values), to instrument. The client library may re-load the dynamic configuration at the capture points, which may change the frequency of tracing a method and its associated information. The dynamic configuration may be defined per method, argument, variable, and/or data structure. The dynamic configuration may be initially deployed with default heuristics defined in the client library.

Abstract: Various methods, apparatuses/systems, and media for automatically generating fully integrated regression and system testing (FIRST) analytics are disclosed. A processor accesses a production database to obtain production data associated with an application, and accesses a user acceptance testing (UAT) database to obtain UAT data associated with the application. The processor generates gap data on test coverage based on comparing the production data with the UAT data; analyzes the generated gap data; automatically generates, in response to analyzing the generated gap data, executable full coverage of test scenarios for testing the application; and automatically executes testing of the application based on the generated test scenarios.

Abstract: A build system stores source code in source code repositories and generates deployment artifacts from a build pipeline. The build system receives requirements associated with certification criteria for certifying a deployment artifact based on the source code. The build system executes a build pipeline including build tasks that compile the source code to generate executable files storing executable instructions. The build system evaluates the certification criteria based on results of execution of the build tasks. The build system determines whether the deployment artifact is a certified deployment artifact based on the result of evaluation of the certification criteria. The build system stores a software asset information document in a source code repository identifying software assets build using the source code repository, for example, as a YAML file.

Abstract: A method includes receiving a high-level function in a high-level code of an application is received. The method also include identifying resources in a hardware to execute a set of low-level instructions that is generated from the high-level function in the high-level code. One or more processing operations are determined to be performed that is associated with the high-level function in the high-level code. The determining of the one or more processing operations occurs based on architecture of the hardware. The high-level function in the high-level code of the application is compiled into the set of low-level instructions to be executed on the hardware. A plurality of structured metadata is generated and includes information associated with the determining resources in the hardware and further includes information associated with the determining one or more processing operations.

Abstract: Methods and systems are presented for testing software applications in a production-like environment that simulates real-world failures of production environments. A production environment has production applications and databases configured to process user requests from users for conducting transactions with a service provider. A testing system provides an intermediate interface that enables a software application operating in the test environment to access at least one of a production application or a production database. The intermediate interface can be configured based on different failure configurations to simulate production component failures in the production environment. Failure injection and randomized failure modes can be employed, including for network-related failures (latency, dropped packets, connections, etc.) that might occur in the production environment.

Abstract: A server that includes circuitry and memory is provided. The memory stores a trained neural network model to provide a first application associated with a first service. The first application is executable on a first electronic device associated with first capability information. The circuitry receives, from a second electronic device, second capability information associated with the second electronic device. The circuitry further generates a second program code based on the received second capability information of the second electronic device and a first program code associated with first application. The circuitry further generates a second application associated with the first service based on the generated second program code. The circuitry further transmits the generated second application to the second electronic device. The generated second application may be executed on the second electronic device to provide the first service on the second electronic device.

Abstract: Regression test case identification for automated regression testing includes identifying program code file(s) in which changes made to program code of an application are contained, selecting application feature(s) of the application as candidate(s) on which to perform automated regression testing, which selecting includes accessing mappings indicating features implemented by the identified program code files and identifying those features as the selected application feature(s), based on the selected application feature(s), selecting regression test cases to be included in the automated regression testing, and commencing execution of the automated regression testing using the selected regression test cases.

Abstract: A method of using artificial intelligence to provide source code from an original programming language in a target programming language showing regions of low confidence. The method includes receiving, by a computing device, a code base in an original programming language. The computing device further provides the code base in the original programming language to a target programming language using an artificial intelligence tool. The computing device additionally displays the code base in the target programming language using a visualization tool in a visual interface. The computing device still further displays the regions of uncertainty to a human user in the visual interface. The regions of uncertainty provide low confidence regions of the code base in the target programming language for targeted user intervention. The regions of low confidence correlate with violations to provide displayed actionable insight regions.

Abstract: Disclosed techniques relate to automatically instrumenting a web application. In an aspect, a method identifies that a web application includes an event that is triggered by a user interaction. The method associates the event with a tracer that is configured to log tracing information based on an execution of a first set of operations caused by the event and to obtain a first measurement of performance of a first span. The method identifies, in the code, that the execution of a first set of operations causes a request to be made to a server. The method associates the request with the tracer. The tracer is configured to log tracing information based on an execution of a second set of operations caused by the request and to obtain a second measurement of performance of a second span that is a child span of the first span.

Abstract: Software application data management. A data stream generated via a software application is received. A user profile is determined for a developer of the software application. One or more complications in a data model of the software application are predicted. Data of the data stream received is managed based on the one or more complications in the data model.

Abstract: A fire and security system includes a control panel and one or more connected device. The control panel is in signal communication with a data control loop. The connected device is in signal communication with the at least one control panel via the at least one data control loop. The connected device includes a memory unit having a free memory area that stores first data and an active memory area that stores second data different from the first data. In response to operating the connected device according to the second data, the at least one connected device receives updated data delivered by the at least one of the control panel over the data control loop, and replaces the first data stored in the free memory data with the updated data.

Abstract: Devices at different geolocations are configured to determine and share information regarding execution of an application under various conditions. A device executing the application, or another device connected to the device executing the application, may send data determined by executing the application to a central device. The central device may in turn receive requests from other devices and provide the determined data to the other devices. The devices may be configured to exchange data using native debugging or bridge software on the devices by determining communication parameters that are common to each device, such as protocols, networks, intermediate devices through which communications may be passed, and so forth. To reduce the amount of data that is transmitted portions of the data related to communications between devices, such as acknowledgement codes, may be identified and withheld from sending.

Abstract: A system can monitor applications and analyze the metrics to determine if one or more of the applications are regressing or performing as expected. The metric analysis includes performing a first short term data analysis and, if data is not as expected, a second short term analysis based on machine learning-based pattern recognition machines. If the short-term analysis finds the metrics aren't as expected, a long-term analysis is performed. The long-term analysis can compare chunks of streaming metric data to cached metric blocks and historical data, and can include a concept drift analysis.

Abstract: A computing device including a processor configured to receive source code including a plurality of source code modules. The processor may generate executable code from the source code and assign two or more reload indicators to two or more executable code modules. The processor may execute the executable code. During execution of the executable code, the processor may receive a source code update and generate an executable code update from the source code and the source code update. The processor may apply the executable code update to the executable code to generate updated executable code. The processor may generate a reload ordering of two or more reload operations corresponding to the reload indicators. As specified by the reload ordering, the processor may perform the two or more reload operations at the two or more respective executable code modules. The processor may execute the updated executable code.

Abstract: Among other things, embodiments of the present disclosure improve the functionality of computer software and systems by facilitating the automatic performance optimization of a software application based on the particular platform upon which the application runs. In some embodiments, the system can automatically choose a set of parameters or methods at run-time from a design space with pre-selected optimization methods and parameters (e.g., algorithms, software libraries, and/or hardware accelerators) for a specific task.

Abstract: An offloading server includes: a data transfer designation section configured to analyze reference relationships of variables used in loop statements in an application and designate, for data that can be transferred outside a loop, a data transfer using an explicit directive that explicitly specifies a data transfer outside the loop; a parallel processing designation section configured to identify loop statements in the application and specify a directive specifying application of parallel processing by an accelerator and perform compilation for each of the loop statements; and a parallel processing pattern creation section configured to exclude loop statements causing a compilation error from loop statements to be offloaded and create a plurality of parallel processing patterns each of which specifies whether to perform parallel processing for each of the loop statements not causing a compilation error.

Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.

Abstract: A method for debugging a program, based on simulating an object program and comparing simulated waveforms with standard waveforms are applied in an electronic device. A simulated environment corresponding to the object program is established and multiple instructions from code of the object program are mapped against the standard waveforms. Trigger points are set in the object program, the object program is run from the trigger point and simulation waveforms are stored. The simulation waveforms are compared with the standard waveforms, and the location of a bug of the object program is found according a comparison. The bug may be resolved or cured. The electronic device utilizing the method is also disclosed.