Abstract: Disclosed is a computer program stored in a computer readable storage medium, the computer program including commands which cause a processor of a server to execute operations below, the operations including: if a first program written in a source language is converted to a second program written in a target language, generating block information for the first program by analyzing the first program; inserting an annotation into the second program, wherein the annotation includes information on at least one source line, which is included in the first program, associated with at least one target line included in the second program; generating meta information associated with runtime verification based on the block information and the annotation.

Abstract: Analyzing or debugging applications is provided. The system identifies an action for an application provided by a developer. The system determines a first classification score based on historical execution of the action. The system generates a machine generated action for the application based on metadata associated with the application. The system determines a second classification score based on a comparison of the action with the machine generated action. The system selects, via a matching program, a second application that matches the application. The system determines a third classification score based on a comparison of an action approved for the second application with the action provided by the application developer. The system updates a delivery control parameter based on the first classification score, the second classification score and the third classification score. The system controls delivery of the application based on the delivery control parameter.

Abstract: The disclosure herein describes generating a virtualized stub service based on deep learning for use in simulating a service during testing of a software module configured to interact with the service. Request-response pairs between the software module and the service are captured by an interceptor module, each request-response pair including a request and a response to the request. The captured request-response pairs are added to a training data set. A service virtualization engine trains a request categorization model based on the training data set, wherein the request categorization model is trained to respond to requests with responses based on the request-response pairs of the training data set. Then, a virtualized stub service is generated for the service based on the trained request categorization model. The disclosure provides for automatic generation of virtualized stub services using deep learning techniques for use in testing software modules.

Abstract: A computer implemented method for scanning source code files included in an application. A source code scanner identifies an assembly generated from a set of source code files. The assembly comprises assembly code and assembly metadata. The source code scanner identifies a file path for each source code file identified from the assembly. The file path is identified within the assembly metadata. Responsive to identifying the file paths from the assembly metadata, the source code scanner identifies the set of source code files within a code repository. The source code scanner scans the set of source code files to identify potential code vulnerabilities in the set of source code files. The scan omits files in the code repository that were not identified within the assembly metadata.

Abstract: Analyzing or debugging applications is provided. The system identifies an action for an application provided by a developer. The system determines a first classification score based on historical execution of the action. The system generates a machine generated action for the application based on metadata associated with the application. The system determines a second classification score based on a comparison of the action with the machine generated action. The system selects, via a matching program, a second application that matches the application. The system determines a third classification score based on a comparison of an action approved for the second application with the action provided by the application developer. The system updates a delivery control parameter based on the first classification score, the second classification score and the third classification score. The system controls delivery of the application based on the delivery control parameter.

Abstract: According to an aspect of an embodiment, operations include receiving first log data including first information associated with electronic trigger events from a trigger data source. The operations further include extracting trigger output parameters from a first sentence token of the received first log data and displaying a first set of trigger rules onto an electronic User Interface (UI) of a user device based on the extracted trigger output parameters. The operations further include receiving a first input comprising a selection of one or more trigger rules of the displayed first set of trigger rules and receiving a second input comprising a selection of computer-executable actions to be performed based on whether the first information satisfies the one or more trigger rules. The operations further include generating a computer-executable mashup code for execution on a runtime system based on the received first input and the received second input.

Abstract: A code converter uses machine learning to determine conflicts and redundancies in software code. Generally, the code converter uses machine learning to convert software code into vectors that represent the code. These vectors may then be compared with other vectors to determine similarities between code. The similarities may be used to detect conflicts and/or redundancies created during the development process (e.g., when a developer attempts to change the code).

Abstract: A system for customizing and testing software application platforms is provided.

Abstract: Methods and systems are presented for consuming different data sources, and deploying artificial intelligence and machine learning programs on different target devices or infrastructures. Many data types can be transformed into machine learning data shards (MLDS) while many machine learning programs written in various programming languages or frameworks are transformed to common operator representations. Operator representations are transformed into execution graphs (EG) for a chosen target device or infrastructure. The MLDS and EG are input to the targeted devices and infrastructures, which then execute the machine learning programs (now transformed to EGs) on the MLDS to produce trained models or predictions with trained models.

Abstract: A computer implemented method uses memory coherence to enhance latency and bandwidth performance, the method including receiving, by a host, a call from an application. The method also includes, determining that the call includes a device allocation command, wherein the device allocation command is configured to allocate a set of data on a graphical processing unit. The method further includes intercepting the call. The method includes, initiating an alternate data allocation command; and returning the alternate data allocation command to the application. Further aspects of the present disclosure are directed to systems and computer program products containing functionality consistent with the method described above.

Abstract: Embodiments disclosed herein relate to method and systems for updating hardware tables. Such methods may include receiving a flexible pipeline definition from a controller at a compiler; generating, by the compiler, a hardware table update initial instruction set; and transmitting the hardware table update initial instruction set to a network device. The network device may use the hardware table initial instruction set to configure the network device to implement the flexible pipeline definition for a network chip.

Abstract: A tracing apparatus communicably connected to a programmable logic controller (PLC) that inputs and outputs a signal based on a ladder program, includes a constituent element setting unit configured to set a plurality of constituent elements to be traced among the constituent elements of the ladder program, a tracing data acquisition unit configured to acquire tracing data indicating a signal state of the constituent element at a specified timing, at a predetermined sampling cycle, a section detection unit configured to detect a sampling cycle section in which two or more of the acquired tracing data corresponding to the constituent elements simultaneously change, a change sequence calculation unit configured to calculate change sequence of signals of the constituent elements simultaneously changing in the detected sampling cycle section, and a change sequence output unit configured to output the calculated change sequence of the signals of the constituent elements.

Abstract: A method, a system, and a computer program product for generating and using contextual and adaptable parameters for monitoring of various processes. A plurality of standard parameters for use during execution of a computing process by a computing system are received. At least one standard parameter in the plurality of standard parameters is modified in accordance with one or more contextual features associated with the execution of the computing process. The computing process is executed by the computing system using a combination of at least one standard parameter in the plurality of standard parameters and the at least one modified parameter.

Abstract: Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for AI enhanced mobile integration (AMI). In an embodiment, a method comprises: receiving, by one or more processors, a user-driven integration request and a binary executable for a mobile application; analyzing, by the one or more processors, the binary executable to determine an integration plan; obtaining or creating fusion components based on the integration plan; and creating a second binary executable for the application using the obtained or created fusion components.

Abstract: According to an aspect of an embodiment, a method may include obtaining a computer-readable program and analyzing the computer-readable program to identify a constant in code of the computer-readable program. The method may also include obtaining context data associated with the constant from a portion of the code that includes an occurrence of the constant. The method may also include determining a location in the computer-readable program of the occurrence of the constant and analyzing the context data to identify a property of potential inputs to the computer-readable program at the location. The method may also include generating an input for the computer-readable program based on the constant and the identified property and providing the generated input to the computer-readable program during execution of the computer-readable program when execution of the computer-readable program reaches the location.

Abstract: Methods, apparatus, and processor-readable storage media for system program change detection via parallel monitoring techniques are provided herein. An example computer-implemented method includes determining multiple user interface elements to monitor at each of one or more action points during execution of at least one system program within an automated testing framework, wherein the at least one system program is designed for operation across multiple at least partially interconnected system devices. The method also includes monitoring, in parallel, at a given one of the one or more action points, for changes to the multiple user interface elements corresponding to the given action point, and performing, based at least in part on the monitoring and on processing of one or more data structures, at least one action within the automated testing framework in response to detection of a change to any one of the multiple user interface elements.

Abstract: Certain aspects of the present disclosure provide techniques for automating testing of applications. One example method generally includes obtaining a user interface (UI) source from an application defining pages of the application and fields associated with UI elements of each page of the pages and receiving a selection of a scenario. The method further includes generating scenario data based on a section of test data associated with the selection of the scenario and executing a test using the scenario data. To execute the test, the method includes identifying, by a runner, a first UI element of a first page of the application, identifying, by the runner, a first element of the scenario data mapped to the first UI element and entering, by the runner, the first element of the scenario data into the first UI element.

Abstract: Embodiments of the present disclosure provide a method, and apparatus and a computer readable medium for controlling a storage system. For example, a method for controlling a storage system is provided. The method comprises: determining a control factor related to a background operation in the storage system, the control factor including a usage state of a storage resource pool in the storage system; setting a priority level for the background operation based on the determined control factor; and controlling execution of the background operation based on the set priority level. Embodiments of the present disclosure may accelerate execution of background operations, improve resource utilization efficiency, and ensure good I/O performance.

Abstract: A system and method for optimizing runtime environments for applications by running the applications in a plurality of runtime environments and iteratively selecting and creating new runtime environments based on a fitness score determined for the plurality of runtime environments.

Abstract: A method and system for providing language services to a client system in a web-based code development environment. A set of code elements corresponding to a first code segment is stored in a local cache of the client system. In response to input of a character associated with the first code segment, a portion of the set of code elements corresponding to the character is displayed.