Techniques are described herein for using artificial intelligence to “learn,” statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks (“GANs”), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (“GNNs”), graph attention neural networks (“GANNs”), graph convolutional networks (“GCNs”), etc., although this is not required.

Methods and apparatuses are provided for code refactoring. The method includes acquiring a code and identifying, using processing circuitry and based on a Markov decision process model, a refactoring sequence. The refactoring sequence includes a plurality of refactoring steps to be applied to the code. Further, the method includes refactoring, by the processing circuitry, the code according to the refactoring sequence.

Systems and methods for identifying a characteristic of an input code by converting the input code into simplified code and using the simplified code to generate snippets that can be compared to code in a database. Preferably, code is simplified by at least one of: unifying of capitalization, removing characters, and replacing at least one of a character and a keyword with an identifier.

Systems and methods for identifying a characteristic of an input code by converting the input code into simplified code and using the simplified code to generate snippets that can be compared to code in a database. Preferably, code is simplified by at least one of: unifying of capitalization, removing characters, and replacing at least one of a character and a keyword with an identifier.

The present invention provides a software management device capable of converting a term used in a model and an abstraction level thereof. A software management device 1 includes: an input unit 2 that inputs a target model; a storage unit 3 that hierarchically stores functions and/or names constituting the model; and an in-model name replacement unit 6 that selects a corresponding function and/or name from the storage unit 3 according to the input model input from the input unit 2, and replaces a function and/or a name in the input model with the selected function and/or name.

A system, method, and computer program product for implementing software modernization and refactoring is provided. The method includes analyzing source code. In response, components and associated interconnections of the source code are identified and a runtime associated with a software application is analyzed. Likewise, components and associated interconnections of the runtime are identified and architectural data is analyzed with respect to the source code and runtime. In response, a software and hardware model associated with operation of the server and software application is generated and the software and hardware model is correlated with results of analyzing the architectural data, source code, and runtime. A dashboard graphical user interface and refactoring model code associated with a modernization and refactoring process configured to generate refactored code are generated and the refactoring model code is executed. In response, refactored code of the software application is generated thereby operationally modifying the software application.

A system, method, and computer program product for implementing software modernization and refactoring is provided. The method includes analyzing source code. In response, components and associated interconnections of the source code are identified and a runtime associated with a software application is analyzed. Likewise, components and associated interconnections of the runtime are identified and architectural data is analyzed with respect to the source code and runtime. In response, a software and hardware model associated with operation of the server and software application is generated and the software and hardware model is correlated with results of analyzing the architectural data, source code, and runtime. A dashboard graphical user interface and refactoring model code associated with a modernization and refactoring process configured to generate refactored code are generated and the refactoring model code is executed. In response, refactored code of the software application is generated thereby operationally modifying the software application.

A management apparatus and a method for managing a software development and maintenance system are provided. In order to improve the quality and to minimize errors in a code base, an analysis of individual parts of the code base and related functional and/or architectural concerns is performed.

Machine logic to change steps included in and/or parameters/parameter value used in artificial intelligence (“AI”) pipelines. For example, the machine logic may control what types of data (for example, sensor data) are received by the AI pipeline and/or have the data is culled in the pipeline prior to application of a machine learning and/or artificial intelligence algorithm.

A method, system, and apparatus are disclosed for adding library models to a library knowledge base by defining a template for a library configuration file that conveys information about each library model, custom inputs and code snippets to facilitate library comparison operations, and education content for the library model, where the library configuration file template may be automatically filled by populating selected data fields in the template with information identifying the library model, scraping documentation pages to extract test cases, and then scraping test case code to extract the test case input parameters for input to an input/output matching engine to evaluate a repository of code snippets and identify a set of functionally similar code snippets for inclusion one or more data fields in the template.