A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

A program generation apparatus includes a generation unit that inputs a specification of a program to be generated described in natural language into a model trained on a relationship between a specification of a program described in natural language and the program to generate a first program, and a change unit that changes the first program to generate a second program satisfying a set of one or more input values and output values, and thus the possibility of a desired program being automatically generated can be increased.

A program generation apparatus includes a generation unit that inputs a specification of a program to be generated described in natural language into a model trained on a relationship between a specification of a program described in natural language and the program to generate a first program, and a change unit that changes the first program to generate a second program satisfying a set of one or more input values and output values, and thus the possibility of a desired program being automatically generated can be increased.

Techniques are described for enabling the automatic refactoring of software application source code to mitigate identified anti-patterns and other software modernization-related issues. A software modernization system analyzes software applications to generate various types of modernization report information, where the report information can include identifications of various types of design and cloud anti-patterns, proposed decompositions of monolithic applications into subunits, refactoring cost information, recommended modernization tools and migration paths, among other such information. A software modernization system further includes a refactoring engine that can automatically refactor source code based on such application analysis information, e.g., to automatically address identified anti-patterns, restructure code for decomposition, etc. A refactoring engine performs refactoring actions based on refactoring templates, machine learning (ML) refactoring models, or other input.

Techniques are described for enabling the automatic refactoring of software application source code to mitigate identified anti-patterns and other software modernization-related issues. A software modernization system analyzes software applications to generate various types of modernization report information, where the report information can include identifications of various types of design and cloud anti-patterns, proposed decompositions of monolithic applications into subunits, refactoring cost information, recommended modernization tools and migration paths, among other such information. A software modernization system further includes a refactoring engine that can automatically refactor source code based on such application analysis information, e.g., to automatically address identified anti-patterns, restructure code for decomposition, etc. A refactoring engine performs refactoring actions based on refactoring templates, machine learning (ML) refactoring models, or other input.

A mixed mode programming method permitting users to program with graphical coding blocks and textual code within the same programming tool. The mixed mode preserves the advantages of graphical block programming while introducing textual coding as needed for instructional reasons and/or for functional reasons. Converting a graphical code block or group of blocks to a textual block lets the user see a portion of the textual code in the context of a larger program. Within one programming tool the mixed mode method allows users to learn programming and build purely graphical blocks; then transition into mixed graphical and textual code and ultimately lead to their ability to program in purely textual code. The mixed mode further allows users to program using any combination of drag-and-drop graphical blocks and typed textual code in various forms.

A mixed mode programming method permitting users to program with graphical coding blocks and textual code within the same programming tool. The mixed mode preserves the advantages of graphical block programming while introducing textual coding as needed for instructional reasons and/or for functional reasons. Converting a graphical code block or group of blocks to a textual block lets the user see a portion of the textual code in the context of a larger program. Within one programming tool the mixed mode method allows users to learn programming and build purely graphical blocks; then transition into mixed graphical and textual code and ultimately lead to their ability to program in purely textual code. The mixed mode further allows users to program using any combination of drag-and-drop graphical blocks and typed textual code in various forms.

A method and system for generating engineering diagrams in an engineering system includes receiving specification of one or more physical components. Further, the method includes obtaining, from a data source, a first engineering diagram representing a portion of a technical installation. The method further includes identifying a deviation in the one or more physical components, physical connections and the parameter values in the first engineering diagram based on the specification of the one or more physical components. Furthermore, the method includes generating an engineering diagram analytics model for the first engineering diagram based on the identified deviation in the one or more physical components, the physical connections and the parameter values in the first engineering diagram. Also, the method includes generating a second engineering diagram representing the upgraded portion of the technical installation based on the generated engineering diagram analytics model.

Source code routines are generated for storage management in a storage code development management tool. A script that includes the source code routines is generated. The storage code development management tool receives indications based on an execution of an object code generated via execution of the script. The storage code development management tool modifies the source code routines based on the received indications.