Can a domain-specific language improve program structure comprehension of data pipelines? A mixed-methods study

TL;DR

This study investigates how a domain-specific language impacts the understanding of data pipeline programs, showing it improves correctness and user perception, thus aiding domain experts in collaborative data engineering.

Contribution

It provides empirical evidence that domain-specific languages enhance program comprehension and user experience in data pipeline development.

Findings

Participants were more correct in understanding program structure using the domain-specific language.

Participants reported better pipeline overview and more structured code with the domain-specific language.

The study suggests domain-specific languages lower barriers for domain experts in data engineering.

Abstract

In many application domains, domain-specific languages can allow domain experts to contribute to collaborative projects more correctly and efficiently. To do so, they must be able to understand program structure from reading existing source code. With high-quality data becoming an increasingly important resource, the creation of data pipelines is an important application domain for domain-specific languages. We execute a mixed-method study consisting of a controlled experiment and a follow-up descriptive survey among the participants to understand the effects of a domain-specific language on bottom-up program understanding and generate hypotheses for future research. During the experiment, participants need the same time to solve program structure comprehension tasks, but are significantly more correct when using the domain-specific language. In the descriptive survey, participants describe reasons related to the programming language itself, such as a better pipeline overview, more enforced code structure, and a closer alignment to the mental model of a data pipeline. In addition, human factors such as less required programming experience and the ability to reuse experience from other data engineering tools are discussed. Based on these results, domain-specific languages are a promising tool for creating data pipelines that can increase correct understanding of program structure and lower barriers to entry for domain experts. Open questions exist to make more informed implementation decisions for domain-specific languages for data pipelines in the future.