Abstract: Automatically generating code and related artifacts from an abstract model of a legacy database, an entity relationship diagram, or other schema. The model may be analyzed to detect normalization, rationalization, naming conventions, structure conventions, and other anomalies and may be used to suggest scripted solutions for resolving the discovered anomalies. The generated code may be exposed and further extendable.

Abstract: A method or system uses AI-based enhancement to improve an initial model, such as an abstract model of a database, before the abstract model is submitted to an automated code author. The AI, which may be a generative AI, can assist with identifying parts of the abstract model (such as UML artifacts) and suggest revisions, according to a series of interactions between a developer and AI. The result of this interaction with the AI is then used to revise or augment the abstract model. Ultimately, the AI-enhanced model is consumed by a code author to generate application source code.

Abstract: Automatically generating code and related artifacts such as application programming interfaces (APIs) and related documentation from an abstract model of a database. The abstract model is derived from a physical model which may be a source such as a legacy database, an entity relationship diagram, or other schema defining the data tables, objects, entities, or relationships etc. of the source. The generated code may be exposed (that is, made visible to the developer in its pre-compiled state) and further configurable and extendable. Any such extended code is maintained separately from generated code. An API and related documentation are also generated from the same abstract model.

Abstract: Automatically generating code and related artifacts such as application programming interfaces (APIs) and related documentation from an abstract model of a database. The abstract model is derived from a physical model which may be a source such as a legacy database, an entity relationship diagram, or other schema defining the data tables, objects, entities, or relationships etc. of the source. The generated code may be exposed (that is, made visible to the developer in its pre-compiled state) and further configurable and extendable. Any such extended code is maintained separately from generated code. An API and related documentation are also generated from the same abstract model.

Abstract: Automatically generating code from an abstract model of a database. The abstract model is derived from a physical model which may be a source such as a legacy database, an entity relationship diagram, or other schema defining the data tables, objects, entities, or relationships etc. of the source. The generated code exhibits several patterns, interfaces and/or features including (a) separation of generated and developer code (b) context patterns (c) response/action patterns (d) language, database interfaces, operating systems and/or (e) user interface patterns.

Abstract: Before generating code from an abstract model of a data store, the model is first analyzed to detect normalization, rationalization, naming conventions, structure conventions, and other anomalies. The analysis is scored, and the score may be weighted. The analysis also suggests scripted solutions for resolving the discovered anomalies. A developer may then choose to implement one or more of the suggested solutions prior to code generation. The score may be compared to a threshold and result used to gate subsequent actions. For example, generation of code from the abstract model may be prevented until such time as anomalies are sufficiently addressed so that the score meets a minimum threshold score.