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 of parameters that can adversely impact the results
of the experiments and potentially lead to wrong conclusions (Type I errors,
i.e., incorrectly rejecting the Null Hypothesis). To this end, we survey the
related literature and identify 10 commonly adopted empirical evaluation
hazards that may significantly impact experimental results. We then perform a
sensitivity analysis on 30 influential studies that were published in top-tier
SE venues, against our hazard set and demonstrate their criticality. Our
findings indicate that all 10 hazards we identify have the potential to
invalidate experimental findings, such as those made by the related literature,
and should be handled properly. Going a step further, we propose a point set of
10 good empirical practices that has the potential to mitigate the impact of
the hazards. We believe our work forms the first step towards raising awareness
of the common pitfalls and good practices within the software engineering
community and hopefully contribute towards setting particular expectations for
empirical research in the field of deep learning testing.

这篇论文调查了机器学习测试中常见的实证评估隐患，通过敏感性分析展示了这些隐患对实验结果的重要性，并提出了一套良好的实证实践以减轻这些隐患的影响。这项研究对软件工程社区的共同问题和良好实践进行了初步调查，并在深度学习测试领域为实证研究设定了特定的期望。