A gentle push in the right direction: a nudge-based framework for data quality review in software engineering

Abstract

As data-driven approaches become increasingly prevalent in software engineering, ensuring data quality has emerged as a critical challenge due to its heightened downstream impact. While software systems rely heavily on data for decision-making, automation, and analytics, software engineers often deprioritize data quality, focusing primarily on functionality and performance. This can lead to inaccurate insights, potential software failures, and increased maintenance costs. Existing research has predominantly addressed data quality management as a technical discipline, overlooking behavioural and cognitive aspects that influence how software engineers view data quality. To address this gap, this paper proposes a nudge-based framework, grounded in behavioural science principles, that systematically integrates context-aware digital nudges into the data quality review process to encourage software engineers to adopt better practices. Evaluation was through expert feedback for framework refinement, and a preliminary controlled experiment, where we empirically evaluated the effect of digital nudging, in terms of performance, efficiency, and engagement. Our positive findings provide promising initial empirical insights and pave the way for further research on the use of digital nudging for altering human behaviour in general, and for software engineers in particular. Future work includes further validation of the framework to ensure completeness and accuracy, exploration of nudge adaptation and personalization in alignment with the framework’s components, and integration with existing platforms and AI-powered tools.