In the last decade, several studies have explored automated techniques to estimate the effort of agile software development. We perform a close replication and extension of a seminal work proposing the use of Deep Learning for Agile Effort Estimation (namely Deep-SE), which has set the state-of-the-art since. Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation. We benchmark Deep-SE against three baselines (i.e., Random, Mean and Median effort estimators) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SVM), as done in the original study. To this end, we use the data from the original study and an additional dataset of 31,960 issues mined from TAWOS, as using more data allows us to strengthen the confidence in the results, and to further mitigate external validity threats. The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SVM in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE. The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play lead to an improvement of its accuracy and convergence speed. These results suggest that using semantic similarity is not enough to differentiate user stories with respect to their story points; thus, future work has yet to explore and find new techniques and features that obtain accurate agile software development estimates.

手工卫生对于预防病毒和感染是至关重要的。由于Covid-19的普遍爆发，戴着面具和手工卫生似乎是公众遏制这些病毒的传播最有效的方式。世界卫生组织（世卫组织）建议在八个步骤中推荐一支基于酒精的手摩擦的指导，以确保所有手表的手都完全干净。由于这些步骤涉及复杂的手势，对它们的人为评估缺乏足够的准确性。然而，深度神经网络（DNN）和机器视觉使得能够为培训和反馈的目的准确地评估手摩擦质量。本文介绍了一种具有实时反馈的自动化深度学习的手RUB评估系统。该系统使用在从志愿者收集的视频数据集上培训的DNN架构来评估符合8步指南的遵守情况，并在手动摩擦指南之后的各种肤色和手部特征。测试了各种DNN架构，并且成立型号的模型导致了97％的测试精度的最佳效果。在建议的系统中，NVIDIA Jetson Agx Xavier嵌入板运行软件。在各种用户使用的具体情况下评估系统的功效，并确定具有挑战性的步骤。在这个实验中，志愿者中手摩擦步骤的平均时间是27.2秒，符合世卫组织指导方针。