Abstract: Systems and methods that provide a mechanism to transition static schema to dynamic schema while maintaining backwards capability. Simple removal of static schema elements, followed by replacement with dynamic schema elements, make a third-party code incompatible since the third-party code references schema entities that no longer exist. Provided is a mechanism to decrease the memory use of non-material static schema entities. Transitioning static schema to dynamic schema allows the database to avoid loading non-material schema entities, thereby decreasing overall memory usage.

Abstract: Indirect function call target identification in software is provided. A set of explicit data flows that pass a function address between software modules of a program is determined using an explicit data dependency analysis. A set of indirect function call targets is generated from results of the explicit data dependency analysis and a dynamic execution analysis of the program. The set of indirect function call targets is expanded by identifying similar target functions based on feature embeddings generated by a graph neural network.

Abstract: A computer-implemented method for detecting code regions related to specific processes in an application is provided including obtaining a call-graph of an application, each node of the call-graph representing a program in the application and each edge of the call-graph representing a call between programs, detecting merge points in the call-graph where paths from different entry points are merged, dividing the call-graph into sub-call-graphs at the merge points to create a macro-call-graph, each node of the macro-call-graph representing each of the sub-call-graphs, detecting code regions related to specific processes in each program in the application based on variable names corresponding to the specific processes, setting an initial state to each entry point of the sub-call-graphs, and identifying sub-call-graphs related to each specific process by using dataflow analysis with the initial states and the code regions based on entry states of the sub-call-graphs.

Abstract: Various aspects of this disclosure relate to determining mapping issues in object relational mapping (ORM). An artificial intelligence (AI) model may be trained to identify errors in mapping between relational databases and objects during code compilation. Multiple AI models may be used, with different models being associated with different programming frameworks, thereby making this technique framework agnostic.

Abstract: Systems, methods, and machine-readable media are disclosed for enabling high-performance programming via a composite programming language that gives programmers complete control over the compilation process. The composite programs include two language levels: an object program level (source code), and a metaprogram level that describes how a compiler should be customized in order to optimize the source code for a target hardware environment. When an augmented compiler receives a composite program, it recognizes the metaprogram and implements the one or more parameters specified within the composite program to optimize the compiler for a given target. Once the augmented compiler has been, it proceeds with compiling the source code included in the composite program. The compiled code is then output as machine language and may be executed by one or more computing systems.

Abstract: A system and method may provide assistance to programmers during programming to reduce the number of routine tasks that must be performed. In some aspects, the system may provide for searching a corpus of source code based on keyword or natural language search input. Search results including code entities and snippets of code that are described by the search input are then provided as search results. Some embodiments relate to using a neural network encoder to generate tensor embeddings of source code and related text in a joint tensor space. Relatedness between embeddings in this joint tensor space for text and associated source code is used in some embodiments to facilitate code search.

Abstract: Methods and systems are disclosed that automate and institutionalize many aspects of the process of creating software. Embodiments automate aspects of pricing, software creation, and delivery using a manufacturing-styled approach to development that reuses existing code and other existing software design features.

Abstract: In one example, a method performed by a compiler comprises: receiving a dataflow graph of a neural network, the neural network comprising a neural network operator; receiving information of computation resources and memory resources of a neural network hardware accelerator intended to execute the neural network operator; determining, based on the dataflow graph, iterations of an operation on elements of a tensor included in the neural network operator; determining, based on the information, a mapping between the elements of the tensor to addresses in the portion of the local memory, and a number of the iterations of the operation to be included in a batch, wherein the number of the iterations in the batch are to be executed in parallel by the neural network hardware accelerator; and generating a schedule of execution of the batches of the iterations of the operations.

Abstract: Certain embodiments of the present disclosure provide techniques for performing performance tests against services in a computing environment. The method generally includes deploying application code to an application namespace hosted on a first set of resources in the computing environment. Testing code is deployed to an infrastructure namespace hosted on a second set of resources in the computing environment. A request to test the application code is received. The request generally includes information identifying a load to be generated in testing the application code. A plurality of container instances implementing the test code are instantiated based on the identified load to be generated to test the application code. A test is executed against the application code through the instantiated plurality of container instances.

Abstract: Methods, computer program products, and systems are presented.

Abstract: Techniques are described herein for translating source code in one programming language to source code in another programming language using machine learning. A method includes: receiving first source code in a first higher-level programming language; processing the first source code, or an intermediate representation thereof, using a sequence-to-sequence neural network model to generate a sequence of outputs, each including a probability distribution; generating second source code in a second higher-level programming language by, for each output in the sequence of outputs: determining a highest probability in the probability distribution associated with the output; in response to the highest probability exceeding a first threshold, generating a predicted portion of the second source code based on a token that corresponds to the highest probability; and in response to the highest probability not exceeding the first threshold, generating a placeholder; and outputting the second source code.

Abstract: Implementations are described herein for identifying related source code edits to perform, or to aid in the performance of, various programming tasks. In various implementations, a first edit made to a first source code snippet in a source code editor may be detected. Based on the first edit, a second source code edit to be made to a second source code snippet may be identified. The identifying may include: traversing one or more graphs to determine one or more edge sequences between nodes corresponding to the first and second source code snippets, comparing the one or more edge sequences to a plurality of reference edge sequences between nodes corresponding to historical co-occurrences of the first and second code edits, and identifying the second edit based on the comparing. The source code editor may provide output that includes a recommendation to implement the second edit.

Abstract: Vectorization and scalarization of methods are provided. A plurality of node webs is constructed based on traversing an intermediate representation of a program. Transitive closure of the plurality of node webs is performed to form a set of final node webs. It is determined that each respective node in the set of final node webs can be converted into one of vector operation code or a sequence of scalar operation codes based on at least one node including a specified vector length and only one vector length value being specified within the set of final node webs. Each respective node in the set of final node webs is converted into one of corresponding vector operation code or a corresponding sequence of scalar operation codes to accelerate execution of supported and unsupported methods of the program.

Abstract: Systems, methods, and non-transitory computer readable media are provided for facilitating improved defect resolution. Defect information and defect criteria information may be obtained. The defect information may identify defects of software and/or hardware in development. The defect criteria information may define one or more criteria for measuring the defects. The defects may be measured based on the one or more criteria. A defect analysis interface may be provided. The defect analysis interface may list a limited number of the defects based on the measurements of the defects. The defect analysis interface may provide costs (e.g., computing resources, time, personnel) of solving the defects.

Abstract: Reinforcement learning enables a framework of information technology assets that include software elements, computational hardware assets, and/or, bundled software and computational hardware systems and products. The performance of successive sessions of an inner loop reinforcement learning is directed and monitored by an outer loop reinforcement learning wherein the outer loop reinforcement learning is designed to reduce financial costs and computational asset requirements and/or optimize learning time in successive instantiations of inner loop reinforcement learning training sessions. The framework enables consideration of the license costs of domain specific simulators, the usage cost of hardware platforms, and the progress of a particular reinforcement learning training. The framework further enables reductions of these costs to orchestrate and train a neural network under budget constraints with respect to the available hardware and software licenses available at runtime.

Abstract: Through the systems and methods described herein for provisioning a software automation and tracking performance of the software automation throughout its lifecycle. A realized benefit of deployment of the software automation can be determined and automatically reported according to a schedule. The reports may be provided to certain specified recipients such as project managers, executive officers, sales and/or vendor relations managers, and the like for analysis and processing by the various parties associated with the operation of the software automation. This all-in-one system provides a platform from which one or more software automation projects may be automatically managed through completion and deployment, improving efficiency of the project and management of all deployed software automations for a more cost-effective suite of such programs.

Abstract: To improve the technological process of programming a computer using a dynamic programming language, generate a first portion of training data which maps types in the dynamic programming language to corresponding functions and methods by performing information retrieval on documentation libraries in the dynamic programming language and/or generate a second portion of training data which maps program variables to the corresponding functions and methods by performing data flow analysis on a plurality of pre-existing programs written in the dynamic programming language. Train a neural network on the first and/or second portions of training data to infer unknown types in the dynamic programming language. Carry out inference with the trained neural network to infer the unknown types. Facilitate programming in the dynamic programming language based on the inferred unknown types. Optionally, execute a resulting program.

Abstract: An order is received indicating a network service model. A context of the order is identified. A deployment plan is generated using the network service model, the deployment plan facilitating an instantiation of a contextually-motivated network service instance as a set of normalized lifecycle management (LCM) operations performed against each of a plurality of associated service entities. The deployment plan is deployed, the deploying including binding each of the normalized LCM operations, based on the context of the order, to one or more respective micro-capabilities, each of the respective micro-capabilities having previously been onboarded to the system as one or more corresponding modeled objects capable of being declaratively composed, each of the corresponding modeled objects including a mapping of object properties, object behaviors, and standard LCM operations to one or more existing micro-capabilities of the system.

Abstract: According to one embodiment, a code information storage is configured to store information on a plurality of functions, a calling relationship between the plurality of functions, and code blocks in the respective plurality of functions. A user operation storage is configured to store information of an already read code block. An already read graph generator is configured to generate an already read graph expressing an already read function including the already read code block, all the code blocks included in the already read function, and a calling relationship between the already read code block and the already read function. An expression of the already read code block differs from an expression of the other code block.

Abstract: A system for developing software provides a graphical user interface on a display of a client device, the graphical user interface displaying features from a library of features for a custom software application, implements simulations of a plurality of the features available for demonstration through the graphical user interface, stores blocks of source code for each feature in a source code repository wherein the blocks are adapted to provide an actual application when compiled by developers, receives from the client device, by a server running a software building component, one or more selected features for the software application, automatically integrates, by the software building component, the one or more selected features to generate an integrated feature set based on attributes of each of the selected features and an inter-feature rules set, and generates an interactive visualization of a navigable prototype of the software application based on the integrated feature set.