Code Behavior Modeling for Modernization Bottlenecks
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Solution Overview
Problem
Developers face inefficiencies in understanding and modernizing existing code, as they spend significant time exploring and enhancing previous code, which is time-consuming and inefficient.
Innovation Solution
A system that records and characterizes existing code behavior, generates a behavior model, and uses machine learning to prototype new code in a target language or environment, enabling graphical depiction and deployment with comparison to existing or desired behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If developers manually explore and understand existing code to modernize it, then they can accurately understand code behavior and architecture, but the process becomes extremely time-consuming and inefficient
Solution Approach 1:
The system creates a virtual copy of the existing code's behavior through execution recording and generates a behavior model that replicates the code's functional characteristics. This allows developers to understand code behavior by analyzing the recorded execution traces and generated models rather than manually reading and interpreting the original code, significantly reducing the time required while maintaining accurate understanding of code functionality and architecture
Solution Approach 2:
The system introduces an intermediary layer between the existing code and the developer. This intermediary consists of execution recording mechanisms that capture code behavior dynamically, and behavior model generation that translates complex code operations into simplified representations. This intermediary layer filters and presents only the essential behavioral information needed for modernization, eliminating the need for developers to manually parse through entire codebases
2Reliability
If developers manually prototype and assemble code modules, then they can ensure code correctness and functionality, but the process becomes labor-intensive and slow
Solution Approach 1:
The system performs preliminary actions by automatically recording execution traces and generating behavior models before the actual code modernization process begins. These pre-generated models capture the essential functional requirements and architectural patterns, allowing developers to focus only on translating and assembling code modules rather than discovering requirements through manual exploration, thereby increasing productivity while maintaining reliability
Solution Approach 2:
The system incorporates feedback mechanisms where the generated behavior models are used to validate and guide code module assembly. Developers can compare prototyped modules against the recorded execution traces and generated models to verify correctness, creating a feedback loop that ensures code functionality accuracy while reducing the iterative trial-and-error process typically required in manual modernization
Data Source
AI summary
Various embodiments of the present technology generally relate to the characterization and improvement of software applications. More specifically, some embodiments relate to systems and methods for modeling code behavior and generating new versions of the code based on the code behavior models. In some embodiments, a method of improving a codebase includes recording a run of the existing code, characterizing the code behavior via one or more models, prototyping new code according to a target language and target environment, deploying the new code to the target environment, and comparing the behavior of the new code to the behavior of the existing code. In some implementations, generating new code based on the behavior models includes using one or more machine learning techniques for code generation based on the target language and environment.