Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20)

In the past years, with the development and widespread of digi- tal technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop series addresses that challenge by look- ing at software engineering, education, and arti cial intelligence research elds to explore how they can be combined. Speci cally, this workshop brings together researchers, teachers, and practi- tioners who use advanced software engineering tools and arti cial intelligence techniques in the education eld and through a trans- generational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions. More information at https://easeai.github.io.

Computational Reproducibility via Containers in Psychology

Scientific progress relies on the replication and reuse of research. Recent studies suggest, however, that sharing code and data does not suffice for computational reproducibility —defined as the ability of researchers to reproduce “par- ticular analysis outcomes from the same data set using the same code and software” (Fidler and Wilcox, 2018). To date, creating long-term computationally reproducible code has been technically challenging and time-consuming. This tutorial introduces Code Ocean, a cloud-based computational reproducibility platform that attempts to solve these problems. It does this by adapting software engineering tools, such as Docker, for easier use by scientists and scientific audiences. In this article, we first outline arguments for the importance of computational reproducibility, as well as some reasons why this is a nontrivial problem for researchers. We then provide a step-by-step guide to getting started with containers in research using Code Ocean. (Disclaimer: the authors all worked for Code Ocean at the time of this article’s writing.)

Recent advances in biomedical simulations: a manifesto for model engineering

Biomedical simulations are widely used to understand disease, engineer cells, and model cellular processes. In this article, we explore how to improve the quality of biomedical simulations by developing simulation models using tools and practices employed in software engineering. We refer to this direction as model engineering. Not all techniques used by software engineers are directly applicable to model engineering, and so some adaptations are required. That said, we believe that simulation models can benefit from software engineering practices for requirements, design, and construction as well as from software engineering tools for version control, error checking, and testing. Here we survey current efforts to improve simulation quality and discuss promising research directions for model engineering.