One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.

Rules can be generated for migrating dependencies of a software application. For example, a computing device can receive a source version of a dependency of a software application and a target version of the dependency of the software application. The computing device can compare the source version to the target version to determine a difference between the source version and the target version. The computing device can receive a template for a rule indicating a location in the source version to be modified for the software application to support the target version. The template can include a fillable section. The computing device can populate the fillable section of the template with a value based on the difference between the source version and the target version.

According to an aspect of an embodiment, operations for code enrichment through metadata for code synthesis are provided. The operations include acquiring package data that include source code files and package metadata. The operations further include extracting additional metadata associated with software package and preparing metadata features based on the package metadata and the additional metadata. The operations further include identifying a set of target portions of a source code included in the source code files and updating one or more source code files using the metadata features. Such files are updated by performing at least one of a revision of existing code comments, and an addition of new code comments for the target portions. The operations further include generating a dataset of natural language (NL) text features and respective code features and training a language model on a sequence-to-sequence generation task.

The present application is directed towards systems and methods for cluster-based code analysis and transformation. Cluster-based analysis may group code objects based on their similarity across functional areas, such as where a code object is cloned in multiple areas (e.g. sort functions that are duplicated across areas, or reports or tables that are identical). In some implementations, objects may be grouped into clusters by type, or based on reading from or writing to a common table. In some implementations, clustering at different layers may be possible.

The present disclosure discloses a method for creating an app-capable basic system, such as an app-capable measuring transducer or an app-capable sensor, and a matching app, comprising the steps of: creating one or more interface methods with their name and version that the basic system expects from the app; creating one or more system calls with their name and version that the basic system offers the app; checking the interface methods and system calls for syntactic and semantic correctness; creating interface stubs for the basic system using the interface methods; creating system call stubs for the app using the system calls; creating the basic system by means of the system calls and interface stubs; and creating the app by means of the interface methods and the system call stubs.

A high level programming language provides extensible data parallel semantics. User code specifies hardware and software resources for executing data parallel code using a compute device object and a resource view object. The user code uses the objects and semantic metadata to allow execution by new and/or updated types of compute nodes and new and/or updated types of runtime libraries. The extensible data parallel semantics allow the user code to be executed by the new and/or updated types of compute nodes and runtime libraries.

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.

A method at a computing device including mapping, within a corpus of documents having both natural language terms and computer language terms, each term as a natural language term or a computer language term, thereby creating mapped terms; and applying at least one Latent Dirichlet Allocation (LDA) model to the mapped terms to create topics that correlate the natural language terms and computer language terms.

Various embodiments relate to a method, controller, and machine-readable storage medium for verifying controlled devices attached to the controller including one or more of the following: selecting, using a system model that models a system of devices comprising the controlled devices attached to the controller, a grouping of the system of devices to be tested; conducting a test of the grouping to produce a test result for the grouping, wherein conducting the test comprises transmitting a communication to at least one device associated with the grouping; choosing a graphical representation of a portion of the system model from a plurality of graphical representations based on the graphical representation including a representation of the grouping; and displaying, on a user interface, the graphical representation and an indication of the test result.

A method for the identification of similarities and dissimilarities of mappings between the elements of a first model and the elements of a second model, an element being one of: an object, a link, a node, a class, an attribute, an activity, a flow, etc., wherein the method uses a processor for performing a model mining of the elements of both models in accordance with pre-defined rules and through at least two of the following analyses: a semantic analysis of the elements; a syntactic and/or structural analysis of the elements; a data-based analysis of the elements; and wherein based on these analyses and potentially also based on pre-performed mappings, similarities and dissimilarities mappings between the elements of the first model and the second model are identified and are provided to a user.