Abstract is missing.
- Release engineering in the AI world: how can analytics help? (keynote)Bram Adams. 1 [doi]
- Improving the performance of code vulnerability prediction using abstract syntax tree informationFahad Al Debeyan, Tracy Hall, David Bowes. 2-11 [doi]
- Measuring design compliance using neural language models: an automotive case studyDhasarathy Parthasarathy, Cecilia Ekelin, Anjali Karri, Jiapeng Sun, Panagiotis Moraitis. 12-21 [doi]
- Feature sets in just-in-time defect prediction: an empirical evaluationPeter Bludau, Alexander Pretschner. 22-31 [doi]
- Profiling developers to predict vulnerable code changesTugce Coskun, Rusen Halepmollasi, Khadija Hanifi, Ramin Fadaei Fouladi, Pinar Comak De Cnudde, Ayse Tosun. 32-41 [doi]
- Predicting build outcomes in continuous integration using textual analysis of source code commitsKhaled Walid Al-Sabbagh, Miroslaw Staron, Regina Hebig. 42-51 [doi]
- LOGI: an empirical model of heat-induced disk drive data loss and its implications for data recoveryHammad Ahmad, Colton Holoday, Ian Bertram, Kevin Angstadt, Zohreh Sharafi, Westley Weimer. 52-61 [doi]
- Assessing the quality of GitHub copilot's code generationBurak Yetistiren, Isik Ozsoy, Eray Tuzun. 62-71 [doi]
- On the effectiveness of data balancing techniques in the context of ML-based test case prioritizationJediael Mendoza, Jason Mycroft, Lyam Milbury, Nafiseh Kahani, Jason Jaskolka. 72-81 [doi]
- Identifying security-related requirements in regulatory documents based on cross-project classificationMazen Mohamad, Jan-Philipp Steghöfer, Alexander Åström, Riccardo Scandariato. 82-91 [doi]
- API + code = better code summary? insights from an exploratory studyPrantik Parashar Sarmah, Sridhar Chimalakonda. 92-101 [doi]