Do Research Software Engineers and Software Engineering Researchers Speak the Same Language?

2025-07-03

AI Summary

This study investigates conceptual terminology misalignment between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs), identifying how divergent interpretations of core terms—e.g., “maintainability” and “technical debt”—impede interdisciplinary collaboration. Method: We propose a novel terminology semantic mapping methodology integrating systematic literature review, qualitative comparative analysis, and empirical interviews to systematically identify conceptual alignment points and knowledge gaps. Contribution/Results: We uncover significant terminological and semantic discrepancies across foundational concepts and delineate three critical adaptation domains: shared contextual framing, pedagogical integration of terminology in training, and tool-supported collaborative practices. The study delivers a reusable methodological framework for building a crowd-sourced RSE–SER terminology mapping repository—the first systematic effort to diagnose and bridge the linguistic divide in this emerging interdisciplinary domain.

Technology Category

• Application Domains: Software Engineering
• Knowledge Representation and Reasoning: Knowledge Engineering
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.

Problem

• Identify terminology differences between RSEs and SERs
• Investigate interpretation gaps in SE fundamentals
• Explore collaboration opportunities through terminology mapping

Innovation

• Investigating terminology differences between RSEs and SERs
• Mapping SE fundamentals for aligned concepts and gaps
• Crowd-sourced extension of systematic terminology methodology