ARTIFICIAL INTELLIGENCE FOR CODE OPTIMIZATION FROM SANDBOX TO COLLECTIVE PERFORMANCE: A QUALITATIVE ANALYSIS
DOI:
https://doi.org/10.56238/arev7n9-036Keywords:
Artificial Intelligence, Front-end, Back-end, Computational Optimization, SandboxAbstract
A sandbox is a secure environment where programming lines or codes are executed, tested, and even validated in a safe and isolated space. The development space acts as a functional space, where code can be modified and adjusted without affecting the product. The regulatory sandbox enables innovation and controlled operations. For example, the increasing integration of artificial intelligence (AI) in software development has driven process optimization and code refactoring. In this sense, this research investigates the effectiveness of different AIs in optimizing code performance, comparing the performance of functional codes developed by humans with versions optimized by four AI models covering the front-end and back-end areas at different levels of cyclomatic complexity. The methodology involved submitting code to a standardized refactoring prompt, with efficiency assessed by a quotient that considers accuracy, execution time, and code size (Q = A.t/S). The results indicated a substantial increase in the efficiency of AI-optimized codes compared to the original ones. However, performance variations were observed between AIs and development fronts. The conclusion is that AI is a resource for code optimization, but its effectiveness is influenced by the task context and the specific model used, highlighting the need for a conscious and contextual application of the technology.
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