 | Monter d'un niveau |
Antoniol, G., Canfora, G., Casazza, G., Lucia, A. D., & Merlo, E. (2025). Recovering Traceability Links Between Code and Documentation: a Retrospective. IEEE Transactions on Software Engineering, 1-8. Lien externe
Tambon, F., Moradidakhel, A., Nikanjam, A., Khomh, F., Desmarais, M. C., & Antoniol, G. (2025). Bugs in large language models generated code: an empirical study. Empirical Software Engineering, 30(3), 48 pages. Lien externe
Tambon, F., Nikanjam, A., Zid, C., Khomh, F., & Antoniol, G. (2025). TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models. ACM Transactions on Software Engineering and Methodology. Lien externe
Zampetti, F., Zid, C., Antoniol, G., & Penta, M. D. (2025). The downside of functional constructs: a quantitative and qualitative analysis of their fix-inducing effects. Empirical Software Engineering, 30(1), 9 (43 pages). Lien externe
Pepe, F., Farkas, C., Nayebi, M., Antoniol, G., & Di Penta, M. (avril 2025). How Do Papers Make Into Machine Learning Frameworks: a Preliminary Study on Tensorflow [Communication écrite]. 33rd International Conference on Program Comprehension (ICPC 2025), Ottawa, ON, Canada. Lien externe
Promodya Thirimanne, S., Lemango, E. Y., Antoniol, G., & Nayebi, M. (juillet 2025). One Documentation Does Not Fit All : Case study of TensorFlow Documentation [Communication écrite]. 49th IEEE Annual Computers, Software, and Applications Conference (COMPSAC 2025), Toronto, ON, Canada. Lien externe
Pepe, F., Farkas, C., Nayebi, M., Antoniol, G., & Di Penta, M. (2025). Replication Package of the Paper "How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow" [Ensemble de données]. Lien externe
