Machine Learning Variable Name Expansion for Legacy Code Reverse Engineering
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Solution Overview
Problem
Reverse engineering of program code is complex due to the lack of specific syntactic rules for variable naming, leading to infinite variations and technical debt from multiple developers, making it difficult to understand and maintain legacy codes with cryptic variable names.
Innovation Solution
A system and method using multi-model based abbreviation expansion techniques, including variable name segmentation, co-occurrence resolution, and conflict resolution, to identify meaningful terms from cryptic variable names in program code, leveraging machine learning models to understand and expand variable names and their usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple developers maintain legacy code with different naming conventions, then code functionality is preserved and adaptability is improved, but variable name understanding becomes difficult and device complexity increases
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between cryptic variable names and their meanings. This system uses machine learning models to expand abbreviations and resolve variable names to their full meanings, serving as a translation layer that doesn't require changing the original code while making it understandable.
Solution Approach 2:
The patent replaces manual mechanical analysis of variable names with automated machine learning-based expansion systems. Instead of developers manually deciphering abbreviations like 'CUST-ID' or 'ORD-DATE', the system automatically expands these to their full meanings using trained ML models, substituting human cognitive effort with computational processing.
2Ease of operation
If variable names are abbreviated for brevity, then ease of operation is improved, but loss of information increases and measurement precision decreases
Solution Approach 1:
The system performs preliminary expansion of variable names before code analysis or maintenance activities. By pre-expanding abbreviations to their full meanings using machine learning models, the system ensures that the complete information is available when developers need to understand or modify the code, preventing information loss from the outset.
Solution Approach 2:
The system implements feedback mechanisms where expansion results are validated against code context and usage patterns. If an expansion doesn't make sense in the given context, the system adjusts or seeks alternative expansions, ensuring that the expanded variable names accurately reflect their intended meanings while maintaining coding brevity.
3Device complexity
If traditional code analysis methods are used, then device complexity is reduced, but loss of time increases and productivity decreases
Solution Approach 1:
The patent replaces traditional manual or rule-based code analysis methods with machine learning-based automated expansion systems. These ML models have been trained on large codebases to quickly expand variable names with high accuracy, dramatically reducing the time required to understand legacy code without adding significant system complexity.
Solution Approach 2:
The system performs variable expansion in advance before code maintenance activities begin. By pre-processing and expanding all variable names in the codebase using trained ML models, the system eliminates the time-consuming manual deciphering process during actual code maintenance, thereby improving productivity without complicating the maintenance workflow.
Data Source
AI summary
To understand/reverse engineer the code, knowledge of cryptic terms (variable names) present in the code is mandatory. The reverse engineering to understand the code is a very complex task which has infinite variations. A method and system for identifying meaningful terms in a domain context from a plurality of cryptic forms of a variable name in a program code is provided. A machine learning model that understands the cryptic form of a variable name and relates the co-occurring cryptic terms and expands them is provided. These expanded forms of cryptic terms directly aid in understanding of each term and its usage in a more accurate way. This knowledge is used in many downstream task of reverse engineering the program code. Linking of the multiple usages of the same variable and aims to reduce the gap of naming convention mismatches introduced by developers is provided.