Application Model Build Artifact Generation
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Solution Overview
Problem
Enterprise software systems face challenges in efficiently generating and managing application model build artifacts and documentation across different audiences and subsets, leading to increased development time and errors due to the need for manual documentation and separate management of subsets.
Innovation Solution
An automated method for generating application model build artifacts and documentation based on subsets of an application model, using a computing system that receives inputs identifying subsets and audiences, and processes semantic constructs to produce tailored documentation and artifacts, reducing errors and development time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated generation of application model build artifacts is implemented, then productivity and accuracy are improved, but device complexity increases
Solution Approach 1:
The system performs automated self-generation of application model build artifacts by processing semantic constructs and documentation sources automatically without requiring manual assembly. The artifact generator component enables the system to serve itself by producing artifacts, documentation, and deployment configurations through automated processing of application model inputs.
2Loss of information
If comprehensive documentation is generated for all audiences, then information completeness is improved, but loss of time in processing increases
Solution Approach 1:
The documentation generation process is segmented by target audience, with different documentation sets produced for developers, end users, and administrators. The system divides the comprehensive documentation task into audience-specific portions, allowing parallel processing and reducing overall processing time while maintaining completeness for each audience segment.
Solution Approach 2:
The system performs preliminary identification and categorization of semantic constructs and their associated documentation sources before generating actual documentation. By pre-processing and organizing content according to audience requirements in advance, the system reduces the time needed for final documentation assembly and delivery.
3Adaptability or versatility
If manual assembly of artifacts and documentation is performed, then flexibility and adaptability are improved, but productivity decreases
Solution Approach 1:
The automated artifact generation system incorporates dynamic configuration capabilities that allow customization of generation parameters, output formats, and target audiences without requiring manual assembly. The system adapts to different deployment scenarios and audience requirements through configurable inputs while maintaining automated processing, thus preserving flexibility without sacrificing productivity.
4Adaptability or versatility
If separate management of application model subsets is implemented, then adaptability to different audiences is improved, but device complexity increases
Solution Approach 1:
The artifact generator is designed as a universal system that handles multiple audience-specific artifact generation tasks through a single integrated process. It can generate artifacts for developers, end users, and administrators simultaneously by processing different semantic constructs and documentation sources through the same automated pipeline, eliminating the need for separate management systems for each audience.
Data Source
AI summary
An application model build processor generates one or more application model build artifacts based on an application model. In one example, a method includes receiving inputs identifying application model subsets and audiences, and associating application model subsets with the audiences. The method further includes receiving inputs identifying semantic constructs of the application model with the application model subsets. The method further includes generating application model build artifacts based on the application model and defined subsets. The method further includes generating documentation topics for semantic constructs in the application model based on the semantic construct, the subsets to which it belongs, the audiences associated with those subsets, and other semantic constructs in those subsets.