High Cyclomatic Complexity Makes Code Modernization Through Generative AI Much More Difficult

Here's how

Cyclomatic Complexity is a metric that measures the complexity of a program's control flow. It counts the number of linearly independent paths through a program, which is typically represented using a directed graph called a control flow graph. The higher the Cyclomatic Complexity, the more paths there are in the program.

When it comes to code modernization through generative AI, the objective is to automatically transform legacy code into more efficient and maintainable forms. However, high Cyclomatic Complexity can make this process much more difficult. Here's why:

Increased code paths: High Cyclomatic Complexity means there are numerous possible paths through the code, resulting in a larger solution space for AI models to explore. This increases the complexity of generating accurate and optimal transformations.

Increased likelihood of errors: Each additional path increases the likelihood of introducing new bugs or unintended behavior during code transformation. These errors can propagate during the modernization process and eventually impact the quality of the output code.

Increased runtime complexity: Code paths that have complex control flow structures often require more runtime computation, such as additional branching or nested loops. AI models need to consider these complexities during the transformation process, which can lead to increased computational overhead or inefficient output code.

Reduced readability and maintainability: High Cyclomatic Complexity often indicates convoluted code with multiple conditional statements and nested control structures. Transforming such code into more maintainable forms becomes challenging as AI models need to balance the trade-off between optimized performance and readable code.

Limited training data: Training generative AI models requires a large dataset of input-output pairs, where the input is legacy code, and the output is the modernized version. As the codebase's complexity increases, it becomes challenging to curate a diverse and representative training dataset. This limitation can affect the AI model's ability to generalize and generate accurate modernized code.

To mitigate these challenges, it is essential to have AI models specifically trained and customized to handle code with high Cyclomatic Complexity. Enter modelcode.ai (of course). This can involve building larger and more diverse training datasets, incorporating techniques like symbolic execution or abstract interpretation to handle complex control flows, and designing optimization objectives that balance performance and readability.