Detecting Code Smells using ChatGPT: Initial Insights
This paper describes first insights on the effectiveness of ChatGPT to detect bad smells in Java projects. We use a large dataset comprising four code smells (Blob, Data Class, Feature Envy, and Long Method) classified into three severity levels. We run two different prompts to assess ChatGPT’s proficiency: i) a generic prompt to verify whether the model can detect smells and ii) a prompt specifying the smells classified in the dataset. We apply evaluation metrics in terms of precision, recall, and F1-score to quantify the ChatGPT abilities’ in identifying the aforementioned smells. Our preliminary results the odds of ChatGPT providing a correct outcome with a specific are 2.54 times higher compared to a generic prompt. Moreover, ChatGPT is more effective at detecting smells with critical severity (F1-score reaching 0.52) than smells with minor severity (F1-score equals to 0.43). To conclude, we discuss the implications of our results and highlight future work in leveraging large language models for detecting code smells.
Fri 25 OctDisplayed time zone: Brussels, Copenhagen, Madrid, Paris change
14:00 - 15:30 | Large language models in software engineering IIESEM Emerging Results, Vision and Reflection Papers Track / ESEM IGC at Telensenyament (B3 Building - 1st Floor) Chair(s): Claudio Di Sipio University of l'Aquila | ||
14:00 15mVision and Emerging Results | Debugging with Open-Source Large Language Models: An Evaluation ESEM Emerging Results, Vision and Reflection Papers Track Yacine Majdoub IResCoMath Lab, University of Gabes, Eya Ben Charrada IResCoMath Lab, University of Gabes Link to publication DOI Pre-print | ||
14:15 15mVision and Emerging Results | Multi-language Software Development in the LLM Era: Insights from Practitioners’ Conversations with ChatGPT ESEM Emerging Results, Vision and Reflection Papers Track Lucas Almeida Aguiar State University of Ceará, Matheus Paixao State University of Ceará, Rafael Carmo Federal University of Ceará, Edson Soares Instituto Atlantico & State University of Ceara (UECE), Antonio Leal State University of Ceará, Matheus Freitas State University of Ceará, Eliakim Gama State University of Ceará | ||
14:30 15mVision and Emerging Results | Exploring LLM-Driven Explanations for Quantum Algorithms ESEM Emerging Results, Vision and Reflection Papers Track Giordano d'Aloisio University of L'Aquila, Sophie Fortz King's College London, Carol Hanna University College London, Daniel Fortunato INESC-ID, University of Porto, Avner Bensoussan King's College London, Eñaut Mendiluze Usandizaga Simula Research Laboratory, Norway, Federica Sarro University College London Pre-print | ||
14:45 15mIndustry talk | Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis ESEM IGC Matteo Esposito University of Oulu, Francesco Palagiano Multitel di Lerede Alessandro & C. s.a.s., Valentina Lenarduzzi University of Oulu, Davide Taibi University of Oulu Pre-print | ||
15:00 15mVision and Emerging Results | Detecting Code Smells using ChatGPT: Initial Insights ESEM Emerging Results, Vision and Reflection Papers Track Luciana L. Silva Federal University of Minas Gerais, Janio R. Silva IFMG, João Eduardo Montandon Universidade Federal de Minas Gerais (UFMG), Marcus Andrade IFMG, Marco Tulio Valente Federal University of Minas Gerais, Brazil | ||
15:15 15mIndustry talk | ChatGPT’s Potential in Cryptography Misuse Detection: A Comparative Analysis with Static Analysis Tools ESEM IGC | ||