Development teams in large companies often maintain a huge codebase whose build time can be painfully long in a single machine. To reduce the build time, tools such as Bazel and distcc are used to build the codebase in a distributed fashion. However, in the process of a distributed building, it is normal that certain remote nodes crash due to two types of errors: Out Of Memory (OOM) and Deadline Exceeded (DE) errors. These crashes lead to a time-consuming rebuild, which is also a problem faced by WeiXin Group (WXG) of Tencent Inc., the company that created WeChat. Since existing tools cannot help avoid the OOM and DE errors, we propose PCRLinear, which predicts the memory and time requirement of a C++ file, allowing the original distributed build system to schedule compilation adaptively according to the prediction. Our experiments show that PCRLinear reduces the OOM and DE errors to zero and demonstrates a significant average build performance improvement of 30%.
Predicting Compilation Resources for Adaptive Build in an Industrial Setting
Development teams in large companies often maintain a huge codebase whose build time can be painfully long in a single machine. To reduce the build time, tools such as Bazel and distcc are used to build the codebase in a distributed fashion. However, in the process of a distributed building, it is normal that certain remote nodes crash due to two types of errors: Out Of Memory (OOM) and Deadline Exceeded (DE) errors. These crashes lead to a time-consuming rebuild, which is also a problem faced by WeiXin Group (WXG) of Tencent Inc., the company that created WeChat. Since existing tools cannot help avoid the OOM and DE errors, we propose PCRLinear, which predicts the memory and time requirement of a C++ file, allowing the original distributed build system to schedule compilation adaptively according to the prediction. Our experiments show that PCRLinear reduces the OOM and DE errors to zero and demonstrates a significant average build performance improvement of 30%.
What Quality Aspects Influence the Adoption of Docker Images?
Docker is a containerization technology that allows developers to ship software applications along with their dependencies in Docker images. Developers can extend existing images using them as base images when writing Dockerfiles. However, a lot of alternative functionally-equivalent base images are available. While many studies define and evaluate quality features that can be extracted from Docker artifacts, it is still unclear what are the criteria on which developers choose a base image over another. In this paper, we aim to fill this gap. First, we conduct a literature review through which we define a taxonomy of quality features, identifying two main groups: Configuration-related features (i.e., mainly related to the Dockerfile and image build process), and externally observable features (i.e., what the Docker image users can observe). Second, we ran an empirical study considering the developers’ preference for 2,441 Docker images in 1,911 open-source software projects. We want to understand (i) how the externally observable features influence the developers’ preferences, and (ii) how they are related to the configuration-related features. Our results pave the way to the definition of a reliable quality measure for Docker artifacts, along with tools that support developers for a quality-aware development of them
