Abstract: A system, computer program product, and method are provided for orchestrating a multi objective optimization of an application. A set of two or more key performance indicators (KPIs) and one or more parameters associated with the application are received. A machine learning (ML) based surrogate function learning model in combination with an acquisition function is leveraged to conduct one or more adaptive trials. Each trial consists of a specific configuration of the one or more parameters. A pareto surface of the KPIs of the application is computed based on the observations of KPI values from each adaptive trial. The pareto surface is explored and an optimal operating point is selected for the application. The application is then executed at the selected operating point.

Abstract: An exemplary method includes solving on a computing system an optimal power flow formulation for a plurality of generators in a power system. The solving includes computing using multi-threaded parallelism a plurality of constraints for the formulation, computing using multi-threaded parallelism a plurality of Jacobian functions of the constraints, and computing using multi-threaded parallelism a Hessian of Lagrangian functions. The method further includes outputting results of the solving, wherein the results comprise values of generation levels for the plurality of generators. Apparatus and program products are also disclosed.

Abstract: Aspects of the disclosure relate to machine learning based test coverage in a production environment. A computing platform may retrieve, for a software application, first log data associated with a production environment, and generate a production navigational graph where a node represents a webpage visited by a user, and an edge represents a navigational link between two webpages. Likewise, second log data associated with a testing environment may be retrieved, and a testing navigational graph may be generated, where a node represents a webpage tested by a developer, and an edge represents a navigational link between two webpages. Then, the computing platform may generate a coverage graph indicative of a gap between the navigation activity and the testing activity. Subsequently, the computing platform may provide, via an interactive graphical user interface, the coverage graph, where nodes and edges of the coverage graph are associated with selectable visual objects.

Abstract: Described are techniques for predictive regression testing. The techniques include a method comprising constructing a call graph of a modified codebase including at least one modified node corresponding to a modified function. The method further comprises generating a subset of codebase tests by removing respective codebase tests that do not call for the at least one modified node. The method further comprises generating respective partial Abstract Syntax Trees (AST) sequences for relevant test paths in the call graph that connect the at least one modified node to one of the subset of codebase tests. The method further comprises inputting, to a machine learning model, the respective partial AST sequences, and generating, based on output from the machine learning model, predicted regression testing results for the relevant test paths.

Abstract: Methods, systems, and computer-readable media are disclosed for dynamic partitioning in distributed computing environments. One method includes: receiving a first data set and a second data set; mapping the first data set into a first set of key-value pairs; mapping the second data set into a second set of key-value pairs; estimating, using a sketch, a frequency count for each key based on the first set of key-value pairs and the second set of key-value pairs; determining whether the estimated frequency count for each key is greater than or equal to a predetermined threshold; and partitioning the key when the estimated frequency count for the key is greater than or equal to the predetermined threshold.

Abstract: A method may include generating, by performing a full analysis of code and for each component of the code, summaries including: (i) a forward summary including a forward flow and (ii) a backward summary including a backward flow, obtaining a modification to a modified component, determining that one of the summaries for the modified component is invalid, and in response to determining that a summary for the modified component is invalid: obtaining the forward flow from the forward summary of the modified component, obtaining the backward flow from the backward summary of the modified component, generating a local flow by performing an incremental analysis of the modified component using the forward flow of the modified component and the backward flow of the modified component, and detecting a defect in the code using the forward flow of the modified component, the local flow, and the backward flow of the modified component.

Abstract: A system, computer program product, and method are provided for supporting risk evaluation and modification of an executable codified infrastructure. The codified infrastructure is analyzed to identify any non-native program instructions. A selection of the identified non-native program instructions are combined and subjected to a risk evaluation by non-native tools. A risk evaluation result is mapped to corresponding lines of the source code, and a risk identifier is assigned to the corresponding lines of the source code. One or more modifications are selectively applied to the codified infrastructure in correspondence with the assigned risk identifier. The applied modification mitigates any defects in the source code.

Abstract: A method for blocking external debugger application from analysing code of software program installed on computing device. The method including initializing software program including an application program and an internal debugger application. The software program, upon initialization thereof, instructs internal debugger application to load application program in internal debugger application. The internal debugger application is configured to utilize kernel resources of an operating system of the computing device.

Abstract: Disclosed are systems and methods for determining developed code scores of an application. The method may include: receiving a shell of developed code for an application including first score tags of first blocks of developed code and second score tags of second blocks of developed code from a first user; storing the received shell of developed code in a database; receiving third score tags of the first blocks of developed code from a second user; identifying patterns in the developed code based on the received third score tags; applying the identified patterns to the second blocks of the developed code; determining fourth score tags for the second blocks of the developed code based on the applied patterns; and updating the shell of developed code based on the received third score tags and the determined fourth score tags.

Abstract: An example system including a processing resource and a memory resource. The memory resource may store instructions executable by the processing resource to receive, at a software-defined node of a computational infrastructure, a policy specifying a node metric criterion associated with executing a workload at the node; collect a present node metric corresponding to the specified node metric criterion at the node; receive a prediction, based on historical data, of a future modification to the present node metric corresponding to the specified node metric criterion at the node; determine, at the software-defined node, an eligibility value of the node to execute the workload based on the specified node metric criterion, the present node metric criterion, and the predicted future node metric criterion; and determine whether to deploy the workload to the node for execution based on the determined eligibility value.

Abstract: Embodiments described herein include methods and/or systems for updating a medical device. Embodiments include medical devices which are configured for updates in response to various events including connection of a peripheral device to the medical device, a user initiated event, or based on received recommendations.

Abstract: A device and a method is provided for the management of software modules and objects, in particular control units of a motor vehicle, in a database in which the objects are stored and in which at least one software module for provision of at least one function of the object, is associated with a respective object. The method includes acquiring compilable source code developed in a programming language at an operating interface for the at least one software module, generating generation of an abstract syntactic structure, in particular an abstract syntax tree, of the software module by syntactic analysis of the source code, storing the abstract syntactic structure in the database, and establishing establishment of at least one logical connection between the object and the abstract syntactic structure of the software module.

Abstract: A timer service receives timer creation requests from clients and fires those timers upon their expiration by returning to the requesting clients payloads provided with the timer creation requests. A timer creation request includes a client identifier, a timer expiration time, and a payload. The timer service queues timer creation requests, a creation worker adds timer records to a timer store in response to requests and manages partitions of the timer store, and sweeper workers, one per partition, sweep timers into a second queue. A firing worker reads timer index records from the second queue, determines the timers referenced in those timer index records and executes the payloads of those timers. The timer store can be tiered, with partitions different storage priorities based on the timer expirations of the timers in the partitions.

Abstract: Disclosed embodiments provide techniques for automatic software defect correction of a computer program. Computer program log files are scanned to identify runtime errors, corresponding to software defects. The software defects are analyzed to determine an error type, and identify the source file/code that caused the error. A solution template repository is searched for a solution template corresponding to the identified error type. If a solution template is found, the source code is checked out from the identified source repository. The template is applied to the “original” checked out source file to create a new source file with the fix, which is then uploaded back to the repository. A new software distribution is automatically built with the new source file, and the new software distribution is automatically deployed to the devices that experienced the error. Thus, defects can be automatically detected, repaired, and deployed without human intervention.

Abstract: An apparatus comprises a processing device configured to monitor traffic associated with a production application in a production environment of a software development platform, to generate software testing plans based on the monitored traffic, to identify software code to be tested for an updated version of the production application, and to select one or more of the software testing plans for testing the identified software code in a staging environment prior to integration testing in an integration testing environment of the software development platform. The processing device is also configured to perform the selected software testing plans on the identified software code in the staging environment, and to deploy the identified software code in the updated version of the production application in the integration testing environment based at on results of performing the selected software plans on the identified software code in the staging.

Abstract: Technology is described for computationally intensive distributed computing systems and methods. A method for using object types to distribute processing may include determining object types for data objects. A mapping between the data objects and processing partitions may be determined using the object types. A processing partition may be associated with a processing application that processes data objects of a given object type. A hardware host in a distributed computing system may identify an application library having a plurality of processing applications that process a plurality of data objects associated with a plurality of object types. The hardware host may use the processing partition assigned to the hardware host to identify the processing application for the object type associated with the processing partition from the application library. The hardware host may launch an instance of the processing application to enable the hardware host to process the plurality of data objects.

Abstract: An approach is provided for automating an adoption of cloud services. A machine learning model is devAppliceloped to learn application source code accessed from a source code repository. The machine learning model is trained by building training data that includes the application source code and technical labels. Services are extracted from the application source code and a context of the application source code is derived. Using a decision tree model, a mapping of the extracted services to respective cloud services is determined. Based on the mapping, portions of the application source code are identified that are recommended to be replaced by the respective cloud services.

Abstract: A computer-implemented method, a computer system and a computer program product for releasable resource-based preemptive scheduling. One or more currently running workloads are determined to be preempted by a pending workload. Releasable resources from the one or more currently running workloads meet required resources of the pending workload. The pending workload is dispatched so that it uses at least part of the releasable resources from the one or more currently running workloads to run.

Abstract: This application relates to a system for the automatic generation of software developer documentation. The system utilizes various machine learning algorithms and processes to facilitate at least a portion of the documentation process. Included are steps for extracting metadata from an undocumented symbol's declaration, processing a corpus of previously documented symbols, and generating output for the undocumented symbol. Consequently, a writer is provided with a document that is at least partially completed when the writer begins the process of documenting an undocumented symbol.

Abstract: A branch element identification unit identifies a branch element being a program element for branching, from a model diagram representing a plurality of program elements and a relationship among the program elements. A group identification unit identifies a branch destination group, the branch destination group being a series of program elements constituting branch destination of the branch element, for each branch destination from the model diagram. A sequence determination unit determines a sequence of the branch destination groups in a program code. A code generation unit generates the program code based on the model diagram. The program code includes codes corresponding to the branch destination groups in accordance with the sequence of the branch destination groups.