Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
IEEE International Symposium on Software Reliability Engineering (ISSRE)
Predicting the quality of software in the early phases of the development life cycle has various benefits to an organization’s bottom line with wide applicability across industry and government. Yet, developing robust software quality prediction models in practice is a challenging task due to “super” extreme class imbalance. In this paper, we present our work on a code quality prediction framework, we call Automated Incremental Effort Investments (AIEI), to fasten the process of going from data to a performant model under super extreme class imbalance. Experiments on a large scale real-world dataset, from Meta Platforms, show that the proposed approach competes with or outperforms state-of-the art shallow and deep learning approaches. We evaluate the practical significance of the model predictions on test case prioritization efficiency, where AIEI achieves the top rank reducing code review time by 2.5% and test case resource utilization by 9.3%.
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Lisa Rivalin, Andrew Grier, Tobias Tiecke, Chi Zhou, Doris Gao, Prakriti Choudhury, John Fabian
Nadia Alshahwan, Mark Harman, Alexandru Marginean