🤖 AI Summary
This study investigates whether domain-specific languages (DSLs) enhance developers’ comprehension of data pipeline program structure. Method: A mixed-methods approach is employed—controlled experiments measure task accuracy, while structured surveys and qualitative coding analyze DSLs’ impact on domain experts’ structural awareness, accessibility, and alignment with mental models. Contribution/Results: This work provides the first empirical validation of systematic improvements in structural understanding of data pipelines afforded by DSLs. Results show statistically significant gains in comprehension accuracy (p < 0.01), driven by DSLs’ capacity to reinforce global program overviews, enforce syntactically constrained structures, and better align with users’ domain-specific mental models. Furthermore, DSLs lower the barrier to entry for programmers with limited experience, facilitate cross-tool knowledge transfer, and strengthen perception of dataflow structure.
📝 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.