ARTICLE
LLMs and Prompting for Unit Test Generation: A Large-Scale Evaluation
- Journal non spécifié : 2464-2465
Lien de l'article :
https://doi.org/10.1145/3691620.3695330
Discipline :
Informatique et sciences de l'information
Auteur(s) :
Wendkûuni C. Ouédraogo, Abdoul Kader Kaboré, Haoye Tian, Yewei Song, Anil Koyuncu, Jacques Klein, David Lo, Tegawendé F. Bissyandé
Auteur(s) tagués :
BISSYANDE T. François D'Assise
Renseignée par : BISSYANDE T. François D'Assise
Résumé
Unit testing, essential for identifying bugs, is often neglected due to time constraints. Automated test generation tools exist but typically lack readability and require developer intervention. Large Language Models (LLMs) like GPT and Mistral show potential in test generation, but their effectiveness remains unclear.
Mots-clés
Test (biology), Scale (ratio), Unit testing, Computer science, Unit (ring theory), Psychology, Operating system, Geography, Mathematics education, Geology