With the advancement of Large Language Models (LLMs) in recent years, automatic code completion has become one of their key applications in software development. This paper examines automatic code evolution with a focus on the Python programming language. In this study, state-of-the-art language models such as GPT-4, OpenAI Codex, and tools such as GitHub Copilot are reviewed, and their capabilities in code completion and automatic code generation are analyzed. Research findings show that large language models have significantly improved the accuracy and speed of code completion and, in some cases, increased developers’ productivity. For example, a 55% reduction in task completion time has been reported (Perry et al., 2023). However, challenges such as the generation of insecure code and the continued need for human oversight of outputs still remain (Pearce et al., 2022). In addition to reviewing the background and findings of domestic and international studies, this paper explains the methodology used to evaluate the performance of these models and presents the results regarding the impact of language models on the Python programming process. Finally, by summarizing the discussion, the paper highlights the opportunities and challenges involved in applying LLM-based programming assistants in software development.
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