Data Structure Validation Across Programming Languages
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Solution Overview
Problem
Ensuring compatibility between data structures in different programming languages is challenging, leading to errors when compiled programs are run on target machines due to inconsistencies in data offsets, resulting in incorrect data reading and storage overlay with invalid data.
Innovation Solution
A method and system for validating data structures by identifying equivalent structures in different programming languages, comparing data definitions, determining incompatibilities, and generating corrective actions, which includes a validation component with sub-components for mapping, comparing, and mitigating incompatibilities, and communicating results to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data structures are manually mapped between different programming languages, then data compatibility can be achieved, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs preliminary validation by comparing data structure definitions across different programming languages before compilation or deployment. The validation component proactively identifies incompatibilities in data offsets, field lengths, and data types, allowing developers to correct issues before they cause runtime errors, thus reducing rework time and improving reliability.
Solution Approach 2:
The patent introduces an intermediary validation component that acts as a mediator between different programming language compilers and the final compiled output. This component automatically compares data structure definitions, identifies incompatibilities, and generates validation reports, serving as a bridge that ensures compatibility without requiring manual intervention in each language's compilation process.
2Measurement precision
If comprehensive manual analysis of data structures is performed, then all offset inconsistencies can be detected, but the complexity and effort required increases significantly
Solution Approach 1:
The validation component performs self-service by automatically comparing data structure definitions against predefined language-specific rules and conventions. It autonomously identifies offset inconsistencies, field length mismatches, and type incompatibilities without requiring complex manual analysis procedures, thereby achieving high detection precision while keeping the validation system relatively simple.
Solution Approach 2:
The system changes the validation approach from manual parameter-by-parameter analysis to automated parameter comparison using predefined rules. By transforming the validation process into a rule-based automated system, it achieves comprehensive offset consistency detection without proportionally increasing system complexity, as the rules encode the analytical logic that would otherwise require manual execution.
3Reliability
If data structure validation is performed across multiple programming languages, then compatibility issues are identified, but the process becomes more complex and difficult to maintain
Solution Approach 1:
The validation component is designed with universality to handle multiple programming languages through a single unified interface. It applies language-specific rules and conventions internally while presenting a consistent validation process to users, thereby ensuring cross-language compatibility without requiring separate complex validation processes for each language pair.
Solution Approach 2:
The system manages multi-language validation complexity by parameterizing language-specific rules and conventions. Instead of hardcoding separate validation logic for each language combination, the system uses configurable parameters and rules that can be adjusted per language, making the validation process adaptable and easier to maintain across different programming languages.
Data Source
AI summary
Embodiments of the invention relate to a method, system, and computer program product for validating a data structure, comprising: responsive to identifying a data structure in a first programming language, identifying an equivalent data structure in a second programming language; comparing a data definition of the data structure with a data definition of the equivalent data structure; determining an incompatibility between a compared data definition of the data structure and a compared data definition of the equivalent data structure; and responsive to determining an incompatibility, identifying a rule defining an action to be taken in order to correct the incompatibility.