Context-Dependent Code Fragment Generation
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Solution Overview
Problem
Current software development techniques face issues such as high software defects, bloating, complexity, unpredictability, and low developer productivity, with existing solutions like 3rd and 4th Generation Languages, Object Oriented Programming, and Formal Methods failing to significantly improve productivity.
Innovation Solution
A method of generating executable code by defining a component combination based on desired functionality, where components communicate to determine context and generate code fragments, which are combined to form executable code, allowing for context-dependent and optimized code generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generalised libraries are used for code re-use, then productivity is improved to some extent, but code size increases significantly and code becomes unoptimised
Solution Approach 1:
The patent applies local quality by generating code fragments with context-specific optimizations for each particular usage scenario, rather than using generalised libraries. Each code fragment is tailored to the specific component combination and execution context, eliminating unnecessary code while maintaining re-usability through the component-based generation approach.
Solution Approach 2:
The system changes parameters by dynamically generating code based on context information including component types, connections, and execution environment. This allows the same high-level component specification to produce differently optimized code fragments depending on the specific context, achieving both productivity and code size reduction.
2Adaptability or versatility
If context-independent code is generated for portability, then adaptability is improved, but code size increases and optimization is lost
Solution Approach 1:
The patent segments code generation into context-independent component specifications and context-dependent code fragments. The component definitions remain portable and reusable, while the generated code fragments are optimized for specific contexts. This segmentation allows portability at the component level while achieving optimization at the code generation level.
Solution Approach 2:
The system introduces dynamics by making code generation adaptive to context rather than static and context-independent. The code generation process dynamically adjusts based on component combinations, connections, and execution environment, producing optimized code that maintains portability through the underlying component-based architecture.
3Reliability
If code is generated from scratch by skilled programmers, then code quality and reliability are maintained, but productivity remains low and development costs are high
Solution Approach 1:
The patent applies self-service by enabling automatic code generation from component specifications, eliminating the need for manual programming while maintaining quality through systematic component-based generation. The system generates reliable code automatically based on defined component relationships and context, reducing both human effort and defects associated with manual coding.
Solution Approach 2:
The system performs preliminary action by pre-defining component specifications, connections, and context information before code generation. This upfront structuring ensures code reliability through systematic design while enabling automated generation that improves productivity. The component models serve as reusable templates that guarantee quality without requiring manual programming for each implementation.
4Manufacturing precision
If manual code creation is used, then code can be highly optimized for specific contexts, but development time and costs become unpredictable
Solution Approach 1:
The system applies self-service by automatically performing context analysis and code optimization without manual intervention. The automated generation process analyzes component combinations and execution contexts to produce optimized code, eliminating the time loss associated with manual optimization while maintaining high manufacturing precision through systematic optimization algorithms.
Solution Approach 2:
The system uses feedback by analyzing execution context and component relationships to guide code generation and optimization. This feedback loop ensures that generated code is highly optimized for specific contexts while the automated process eliminates unpredictable development times, making both optimization and scheduling reliable.
Data Source
AI summary
A method of generating executable code for performing desired functionality. The method includes defining a component combination based on the desired functionality, the component combination being indicative of components to be implemented and connections between the components. The component combination is then used to cause components in the component combination to be implemented, with the components communicating using the connections and generating code fragments. At least two of the components communicate to determine a context, with one or more of the code fragments being at least partially based on the determined context. The generated code fragments are combined to thereby form the executable code which when executed by a suitable a processing system, causes the processing system to perform the desired functionality.