Container-based solutions, such as Docker, have become increasingly relevant in the software industry to facilitate deploying and maintaining software systems. Little is known, however, about how outdated such containers are at the moment of their release or when used in production. This article addresses this question, by measuring and comparing five different dimensions of technical lag that Docker container images can face: package lag, time lag, version lag, vulnerability lag, and bug lag. We instantiate the formal technical lag framework from previous work to operationalise these different dimensions of lag on Docker Hub images based on the Debian Linux distribution. We carry out a large-scale empirical study of such technical lag, over a three-year period, in 140,498 Debian images. We compare the differences between official and community images, as well as between images with different Debian distributions: OldStable, Stable or Testing. The analysis shows that the different dimensions of technical lag are complementary, providing multiple insights. Official Debian images consistently have a lower lag than community images for all considered lag dimensions. The amount of lag incurred depends on the type of Debian distribution and the considered lag dimension. Our research offers empirical evidence that developers and deployers of Docker images can benefit from identifying to which extent their containers are outdated according to the considered dimensions, and mitigate the risks related to such outdatedness.

基于容器的解决方案（例如Docker）在软件行业中变得越来越重要，以促进部署和维护软件系统。然而，对于这种容器在释放时或在生产中使用时的过时程度知之甚少。本文通过测量和比较Docker容器映像可能面临的五个不同方面的技术滞后来解决此问题：包滞后，时间滞后，版本滞后，漏洞滞后和错误滞后。我们实例化了先前工作中的正式技术滞后框架，以在基于Debian Linux的Docker Hub映像上实现滞后的这些不同方面 分配。我们在三年的时间内对140,498张Debian图像进行了技术滞后的大规模实证研究。我们比较了官方图像和社区图像之间的差异，以及具有不同Debian发行版的图像之间的差异：OldStable，Stable或Testing。分析表明，技术滞后的不同维度是互补的，提供了多种见解。对于所有考虑的滞后尺寸，官方 Debian图像始终比社区图像具有较低的滞后。产生的延迟量取决于Debian的类型分布和考​​虑的滞后维度。我们的研究提供了经验证据，表明Docker映像的开发人员和部署人员可以根据所考虑的维度从确定其容器过时的程度中受益，并减轻与此类过时有关的风险。