Automated Source Code Knowledge Extraction via Pattern Matching
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Solution Overview
Problem
Legacy software applications with limited or no documentation pose challenges for migration due to labor-intensive and error-prone manual processes, and existing automation tools produce non-flexible and proprietary outputs, limiting their adaptability and long-term usability across different platforms and programming languages.
Innovation Solution
A method and system that transforms source code by identifying and classifying logic blocks, extracting knowledge elements, and tracing their life cycles, using pattern matching and classification mechanisms to convert the code into a desired target format, enabling automated knowledge extraction and migration between versions or platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to study and migrate legacy source code, then understanding of embedded business knowledge can be achieved, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The patent replaces manual mechanical analysis of source code with an automated computer-based system that uses pattern recognition and data structure analysis to extract business knowledge, thereby eliminating human error while maintaining comprehensive understanding of the legacy code
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a mediator between the legacy source code and the migration target, using structured data models and pattern recognition to accurately capture and transfer business knowledge without direct human intervention
2Productivity
If existing automation tools are used to aid manual migration, then productivity increases, but the output becomes non-flexible and proprietary
Solution Approach 1:
The patent creates a universal automated migration system that can handle multiple programming languages and software platforms through a common data model and pattern recognition framework, making the tool adaptable to diverse legacy systems rather than being proprietary to a single platform
Solution Approach 2:
The patent segments the migration process into distinct phases: pattern recognition, data structure analysis, knowledge extraction, and transformation. This modular approach allows the system to be applied flexibly across different programming languages and platforms while maintaining standardized output
3Ease of manufacture
If traditional methodology is applied to migrate legacy code, then some migration can be achieved, but the same methodology cannot be adapted to different computer languages
Solution Approach 1:
The patent uses parameter changes by adjusting pattern recognition rules and data structure models based on the specific programming language being analyzed, allowing the same core methodology to adapt to different languages through configurable parameters rather than requiring completely different approaches
Data Source
AI summary
The present disclosure includes a system and method for learning (or discovering and extracting) business knowledge from a collection of source code. The collection of source code is abstracted to generate an abstracted data stream, which is then transformed to an Extensible Markup Language (XML) format. The transformed data in XML format can be further converted to target formats or processed to satisfy different needs such as software system documentation, migration, impact analysis and security analysis. The disclosure also includes an implementation and operation for a pattern abstraction engine configured to receive an input data stream and format it for abstraction into a standard format using a pattern matching mechanism. The disclosure also includes an implementation and operation for a contextual pattern decoder engine configured to extract knowledge attributes and contextual taxonomy from classified blocks of an input data stream.