133 citations · 365 across the 73 of their papers we have counts for
13 papers · 1 filter
Large Language Models in Fault Localisation
Yonghao Wu, Zheng Li, Jie M. Zhang +3
Large Language Models (LLMs) have shown promise in multiple software engineering tasks including code generation, program repair, code summarisation, and test generation. Fault loc…
Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations
Salah Ghamizi, Maxime Cordy, Yuejun Guo +2
Much research on Machine Learning testing relies on empirical studies that evaluate and show their potential. However, in this context empirical results are sensitive to a number o…
Evaluating the Robustness of Test Selection Methods for Deep Neural Networks
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Testing deep learning-based systems is crucial but challenging due to the required time and labor for labeling collected raw data. To alleviate the labeling effort, multiple test s…
Active Code Learning: Benchmarking Sample-Efficient Training of Code Models
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), espec…
Time-based Repair for Asynchronous Wait Flaky Tests in Web Testing
Yu Pei, Jeongju Sohn, Sarra Habchi +1
Asynchronous waits are one of the most prevalent root causes of flaky tests and a major time-influential factor of web application testing. To investigate the characteristics of as…
ACoRe: Automated Goal-Conflict Resolution
Luiz Carvalho, Renzo Degiovanni, Matìas Brizzio +4
System goals are the statements that, in the context of software requirements specification, capture how the software should behave. Many times, the understanding of stakeholders o…