Estudo de Assertividade Julia Code

This study evaluates the accuracy and efficiency of the Julia Code project, a solution based on Generative AI for automating code writing within predefined structures. While the system demonstrates the ability to generate functional code, its performance has not been extensively analyzed, making it necessary to validate its results to support decisions regarding its applicability.

The research was conducted through a structured benchmark, where tasks of varying complexity levels (low, medium, and high) were executed. The evaluated metrics include token consumption, the number of correctly implemented files, execution of automated tests, compliance with functional requirements, and adherence to quality standards.

The results indicate that Julia Code is highly effective for low and medium complexity tasks, ensuring correct code generation and successful execution of automated tests. However, in more complex scenarios involving multiple entities, quality requirements were not fully met. The analysis suggests that breaking down complex tasks into smaller subtasks can improve efficiency and compliance with implementation standards.

Thus, the study demonstrates that Julia Code is a promising tool for code automation, provided that its use is adapted according to the complexity level of the implementations.