Best Practices for Developing Computational and Data-Intensive (CDI) Applications

TL;DR

This paper identifies and evaluates best practices for developing high-quality computational and data-intensive applications, emphasizing interdisciplinary collaboration and practical guidelines to improve software quality and scientific progress.

Contribution

It provides a comprehensive set of best practices tailored for CDI applications, evaluated for impact and usability across diverse experience levels.

Findings

Practices have high perceived impact across experience levels.

All evaluated practices are considered easy to apply.

The guide aims to enhance CDI software quality and interdisciplinary collaboration.

Abstract

High-quality computational and data-intensive (CDI) applications are critical for advancing research frontiers in almost all disciplines. Despite their importance, there is a significant gap due to the lack of comprehensive best practices for developing such applications. CDI projects, characterized by specialized computational needs, high data volumes, and the necessity for cross-disciplinary collaboration, often involve intricate scientific software engineering processes. The interdisciplinary nature necessitates collaboration between domain scientists and CDI professionals (Xperts), who may come from diverse backgrounds. This paper aims to close the above gap by describing practices specifically applicable to CDI applications. They include general software engineering practices to the extent that they exhibit substantial differences from those already described in the literature as well as practices that have been called pivotal by Xperts in the field. The practices were evaluated using three main metrics: (1) participants' experience with each practice, (2) their perceived impact, and (3) their ease of application during development. The evaluations involved participants with varying levels of experience in adopting these practices. Despite differing experience levels, the evaluation results consistently showed high impact and usability for all practices. By establishing a best-practices guide for CDI research, the ultimate aim of this paper is to enhance CDI software quality, improve approaches to computational and data-intensive challenges, foster interdisciplinary collaboration, and thus accelerate scientific innovation and discovery.