AI-based code assistants are increasingly popular as a means to enhance productivity and improve code quality. This study compares four AI-based code assistants, GitHub Copilot, Tabnine, ChatGPT, and Google Bard, in method generation tasks, assessing their ability to produce accurate, correct, and efficient code. Results show that code assistants are useful, with complementary capabilities, although they rarely generate ready-to-use correct code.

Corso, V., Mariani, L., Micucci, D., Riganelli, O. (2024). Assessing AI-Based Code Assistants in Method Generation Tasks. In Proceedings - International Conference on Software Engineering (pp.380-381). IEEE Computer Society [10.1145/3639478.3643122].

Assessing AI-Based Code Assistants in Method Generation Tasks

Corso V.;Mariani L.;Micucci D.;Riganelli O.
2024

Abstract

AI-based code assistants are increasingly popular as a means to enhance productivity and improve code quality. This study compares four AI-based code assistants, GitHub Copilot, Tabnine, ChatGPT, and Google Bard, in method generation tasks, assessing their ability to produce accurate, correct, and efficient code. Results show that code assistants are useful, with complementary capabilities, although they rarely generate ready-to-use correct code.
paper
AI-based code assistants; code completion; empirical study;
English
46th International Conference on Software Engineering: Companion, ICSE-Companion 2024 - 14 April 2024 through 20 April 2024
2024
Proceedings - International Conference on Software Engineering
9798400705021
2024
380
381
open
Corso, V., Mariani, L., Micucci, D., Riganelli, O. (2024). Assessing AI-Based Code Assistants in Method Generation Tasks. In Proceedings - International Conference on Software Engineering (pp.380-381). IEEE Computer Society [10.1145/3639478.3643122].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/490700
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