An Empirical Study on the Effects of Jayvee, a Domain‐Specific Language for Data Engineering, on Understanding Data Pipeline Architectures - Heltweg, Philip; Schwarz, Georg-Daniel; Riehle, Dirk; Quast, Felix (Software-Practice & Experience, 2025)
A large part of data science projects is spent on data engineering. Especially in open data contexts, data quality issues are prevalent and are often tackled by non-professional programmers. We introduce and evaluate Jayvee, a domain-specific language for data engineering aimed at reducing barriers to building data pipelines. We show that a structured DSL can have positive effects on speed, ease of use, and quality for data engineering by non-professional developers. For this, we present an empirical quantitative study, in which we compare the performance of students as proxies for non-professional programmers using Jayvee with Python and Pandas. We search for reasons for the empirical findings using a follow-up interview study on how using a DSL changes how non-professional programmers build data pipelines. Participants solve a subset of tasks faster, more easily, and with higher quality when using Jayvee compared to Python. Interviewees describe tradeoffs regarding the DSL's more limited features, stricter code structure, and explicit descriptions. Jayvee is found to be more approachable, which leads to a more guided development flow. New data engineering languages should provide good tooling and documentation, plan how to visualize intermediate data and consider new development workflows involving tools like ChatGPT to find adoptio
Keywords: data engineering; domain-specific language; empirical study; evaluation; open data; programming language

A Systematic Analysis of Problems in Open Collaborative Data Engineering - Heltweg, Philip; Riehle, Dirk (ACM Transactions on Social Computing, 2023)
Collaborative workflows are common in open-source software development. They reduce individual costs and improve the quality of work results. Open data shares many characteristics with open-source software, as it can be used, modified, and redistributed by anyone, for free. However, in contrast to open-source software engineering, collaborative data engineering on open data lacks a shared understanding of processes, methods, and tools. This article presents a systematic literature review of collaboration processes, methods, and tools in data engineering as performed by open data users. An additional interview study with practitioners confirms and enhances the findings and strengthens the resulting insights. We find an ecosystem with heterogeneous participants and no standardized processes, methods, and tools. Participants face a variety of technical and social challenges during their work. Our work provides a structured overview of collaboration systems in open collaborative data engineering, enabling further research. Additionally, we contribute preliminary guidelines for successful open collaborative data engineering projects and recommendations to increase its adoption for open data ecosystems.<br

(untitled) - Heltweg, Philip; Sauerwald, Kai; Beierle, Christoph (19th International Workshop on Non-Monotonic Reasoning NMR)

Challenges to Open Collaborative Data Engineering - Heltweg, Philip; Riehle, Dirk (Proceedings of the 56th Hawaii International Conference on System Sciences, 2023)
Open data is data that can be used, modified, and passed on, for free, similar to open-source software. Unlike open-source, however, there is little collaboration in open data engineering. We perform a systematic literature review of collaboration systems in open data, specifically for data engineering by users, taking place after data has been made available as open data. The results show that open data users perform a wide range of activities to acquire, understand, process and maintain data for their projects without established best practices or standardized tools for open collaboration. We identify and discuss technical, community, and process challenges to collaboration in data engineering for open dat

On using model checking for the certification of iterated belief changes - Sauerwald, Kai; Heltweg, Philip (7th Workshop on Formal and Cognitive Reasoning, FCR 2021, 2021)
Keywords: Belief Change; Total Preorder; Implementation