Research of Neural Networks ChatGPT Used to Generate Code in Python Programming Language

The article deals with the problem associated with the influence of a prompt composed by users on the efficiency of the generated program code. The aim of the research is to work out a methodology allowing to analyze the operation and to estimate the effectiveness of online neural network services g...

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Veröffentlicht in:2024 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED) S. 1 - 6
Hauptverfasser: Leokhin, Yuri, Fatkhulin, Timur, Kozhanov, Mikhail
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 13.11.2024
Schlagworte:
artificial intelligence
automation
Chatbots
Codes
Complexity theory
development
Measurement
methodology
neural network
Neural networks
Optimization
program code
Programming
programming language
Python
Roads
Software development management
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Zusammenfassung:The article deals with the problem associated with the influence of a prompt composed by users on the efficiency of the generated program code. The aim of the research is to work out a methodology allowing to analyze the operation and to estimate the effectiveness of online neural network services generating program code. The object of the study is neural network online services that allow generating program code. The subject of the study is the quality indicators of the generated program code. The relevance of the study is due to the increasing use of neural network technologies, including software development. To achieve this goal, the article considers online neural network services "ChatGPT 3.5", "ChatGPT 4" and "ChatGPT 4o", that allow generating program code in a programming language Python in question-and-answer form, besides, in this study, a methodology has been developed that allows determining the efficiency of neural network tools used to generate program code. In order to test this methodology, tasks of different levels of complexity were introduced into the above-mentioned neural network services and the program code generated by them was analyzed. In conclusion, a comparative analysis of the experimental results is conducted, the advantages and disadvantages of the considered neural network techniques in generating program code are identified. The methodological basis of the article includes the following methods: theoretical analysis, description, comparison and experiment.
DOI:10.1109/TIRVED63561.2024.10769794