# ChatGPT Translation of Program Code for Image Sketch Abstraction
## 5 Motion Detection with ChatGPT-4
It played a crucial role in generating Python code from MATLAB scripts, meticulously ensuring syntax accuracy, and preserving the original algorithmic intent. Its integration with other code conversion tools was seamless, acting as an intelligent assistant that provided real-time feedback and suggestions, thereby enhancing the overall efficiency of the conversion process. By offering innovative solutions and alternative approaches, ChatGPT helped navigate through coding challenges that often required a blend of computational efficiency and human ingenuity. The impact of ChatGPT on the project was evident in the tangible improvements in both the speed and accuracy of the code conversion, leading to a more streamlined and error-resistant output than traditional methods. However, with their extensive experience, the researchers also acknowledged the limitations of ChatGPT. They noted instances where human intervention was essential to resolve complex scenarios beyond the scope of the AI's current capabilities. This balanced perspective highlights the team's understanding of the symbiotic relationship between AI tools and human expertise, particularly in MATLAB to Python code conversion. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criteria** & **Semi-Automated Approach** & **Fully Automated Approach** \\ \hline \multirow{3}{*}{Efficiency} & Time-intensive but allows for greater control during the conversion process. & Faster, as it requires less human intervention. \\ \hline \multirow{3}{*}{Accuracy} & High, due to human oversight correcting potential errors. & May vary, depending on the sophistication of the automation algorithms. \\ \hline \multirow{3}{*}{Ease of Implementation} & More complex, requiring a balance between automated tools and human input. & Simpler, as it relies heavily on pre-defined algorithms. \\ \hline \multirow{3}{*}{Flexibility} & Highly adaptable to specific requirements of different projects. & Less adaptable, often designed for general cases. \\ \hline \multirow{3}{*}{User Involvement} & Requires significant user involvement for decision-making and error correction. & Minimal user input is needed, as the process is largely self-sufficient. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative Analysis of Semi-Automated vs. Fully Automated Methods. ## 7 Conclusions

This section introduces a structured point-by-point summary of the essential findings and implications of the research. It addresses each of the stated research questions (RQ1, RQ2, RQ3, RQ4), emphasizing the significant role of LLMs in the code conversion process and app development. Concluding remarks:

* LLMs as AI Pair Programmers (RQ1): This study confirms that Large Language Models, such as ChatGPT-4, Bard, and Bing, can be effectively used as AI pair programmers in MATLAB-to-Python conversion for complex projects. These models excel in understanding the logic and structure of the code, translating MATLAB code into Python with high accuracy, and providing valuable insights into code functionality. * Comparison with Existing Tool (RQ2): The research shows that LLMs outperform existing language-to-language converter tools regarding accuracy and efficiency. In handling complex MATLAB code, LLMs produced functional, accurate, and efficient Python code more effectively and swiftly than current tools. * Automation Using OpenAI API (RQ3): The study explored the automation of the MATLAB-to-Python conversion process using the OpenAI API. It identified challenges such as token limitations for large code segments and occasional inaccuracies in complex translations. The research suggests segmenting code for translation and combining the API's output with manual review as effective solutions. * Feasibility of Skeleton App Development (RQ4): The feasibility of creating a functional Skeleton App with the help of ChatGPT post-M-to-PY translation is confirmed.