Abstract is missing.
- Message from Program Chairs: NLBSE 2023Sebastiano Panichella, Andrea Di Sorbo. [doi]
- The NLBSE'23 Tool CompetitionRafael Kallis, Maliheh Izadi, Luca Pascarella, Oscar Chaparro, Pooja Rani. 1-8 [doi]
- The (ab)use of Open Source Code to Train Large Language ModelsAli Al-Kaswan, Maliheh Izadi. 9-10 [doi]
- Generalizability of NLP-based Models for Modern Software Development Cross-Domain EnvironmentsRrezarta Krasniqi, Hyunsook Do. 11-13 [doi]
- An Intelligent Tool for Classifying Issue ReportsMuhammad Laiq. 13-15 [doi]
- Few-Shot Learning for Issue Report ClassificationGiuseppe Colavito, Filippo Lanubile, Nicole Novielli. 16-19 [doi]
- Performance Comparison of Binary Machine Learning Classifiers in Identifying Code Comment Types: An Exploratory StudyAmila Indika, Peter Y. Washington, Anthony Peruma. 20-23 [doi]
- Classifying Code Comments via Pre-trained Programming Language ModelYing Li, Haibo Wang, Huaien Zhang, Shin Hwei Tan. 24-27 [doi]
- STACC: Code Comment Classification using SentenceTransformersAli Al-Kaswan, Maliheh Izadi, Arie van Deursen. 28-31 [doi]
- An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test CasesTaryn Takebayashi, Anthony Peruma, Mohamed Wiem Mkaouer, Christian D. Newman. 32-39 [doi]
- Stop Words for Processing Software Engineering Documents: Do they Matter?Yaohou Fan, Chetan Arora 0002, Christoph Treude. 40-47 [doi]
- Applying information theory to software evolutionAdriano Torres, Sebastian Baltes, Christoph Treude, Markus Wagner 0007. 48-55 [doi]
- Zero-shot Prompting for Code Complexity Prediction Using GitHub CopilotMohammed Latif Siddiq, Abdus Samee, Sk Ruhul Azgor, Md. Asif Haider, Shehabul Islam Sawraz, Joanna C. S. Santos. 56-59 [doi]
- Evaluating Code Comment Generation With Summarized API DocsBilel Matmti, Fatemeh Fard. 60-63 [doi]