Execution-Context Code Generation for Low-Resource Software Deployment
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Solution Overview
Problem
Existing software generation systems require specialized skills, lead to unsuitable hardware deployment, high resource consumption, and lack modularity, resulting in technical malfunctions and inefficiencies.
Innovation Solution
A method and device for automatically generating computer instructions that optimize software systems for specific or standardized execution contexts, allowing users to define entities, data structures, and processing operations, and select execution contexts to generate and deploy instructions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If no-code approaches are used to generate software systems, then programming skills are not required and software can be produced easily, but the execution may be unsuitable for the hardware deployment environment leading to technical malfunctions
Solution Approach 1:
The system dynamically adapts the generated software instructions based on the selected execution context. The code generator adjusts the output instructions according to the hardware architecture, operating system, and runtime environment parameters provided by the user, ensuring the software is suitable for the target deployment environment while maintaining ease of generation through graphical interfaces.
Solution Approach 2:
The system changes parameters of the generated code based on the execution context. By accepting user input about hardware architecture, operating system, and runtime environment, the code generator modifies the generated instructions to optimize them for the specific target environment, resolving the contradiction between ease of generation and deployment suitability.
2Ease of manufacture
If no-code approaches are used to generate software systems, then programming skills are not required, but the consumption of software resources (memory) for deployment and execution can be high
Solution Approach 1:
The code generator optimizes resource consumption by adjusting code parameters based on the target execution context. By selecting appropriate programming languages, compilation options, and runtime configurations suited to the hardware architecture and runtime environment, the system generates software that consumes fewer memory and processing resources while maintaining ease of generation.
3Ease of manufacture
If no-code approaches are used to generate software systems, then programming skills are not required, but the consumption of physical resources (electricity) for deployment and execution can be high
Solution Approach 1:
The system optimizes energy consumption by generating code parameters and configurations tailored to the target hardware architecture and runtime environment. By selecting efficient programming languages and optimization levels appropriate for the execution context, the system reduces the physical energy required for deployment and execution while maintaining user-friendly generation processes.
4Productivity
If standardised software systems are generated with fixed templates, then deployment is simplified, but the lack of modularity does not allow standardised software to be generated with customised elements
Solution Approach 1:
The code generation system segments the software generation process into modular components that can be independently configured. Users can select and customize specific modules based on their execution context requirements, allowing both standardized deployment efficiency and adaptable customization through a structured, modular approach to code generation.
5Productivity
If low-code approaches are used with predefined templates, then standardised software systems can be generated from user choices, but the user interaction is limited to marginal adaptation of the structure
Solution Approach 1:
The system dynamically adapts to user needs by providing flexible interaction modes. Users can work with predefined templates for rapid standardized generation when appropriate, or access deeper customization options when specific adaptations are needed. The code generator adjusts its behavior based on the complexity of the requested software, balancing generation efficiency with user interaction flexibility.
Data Source
AI summary
The invention relates to a computer-implemented method (300) for automatically generating instructions configured to be stored in a computer-readable storage medium and to be executed by at least one computer processor, which method comprises:a step (105) of inputting, on a computer interface, at least:a definition of a computer entity,a structure of data associated with the computer entities, anda software processing operation of at least one computer entity,a step (110) of selecting, on a computer interface, an execution context for the instructions,a step (115) of automatically generating, using a computing device, instructions depending on the result of the input step and the selected execution context anda step (120) of storing, using a computing device, the instructions generated.