Operationalizing Software Engineering Theories for Practical Validation

2026-05-04

AI Summary

This study addresses the persistent challenge in software engineering research of empirically validating theories due to the absence of systematic, reproducible operationalization methods. To bridge this gap, the authors propose an integrated methodological framework that combines Sjøberg’s operationalization approach with Dubin’s theory-building methodology, offering the first evidence-driven and replicable guide for operationalizing theoretical constructs in software engineering. The approach systematically translates abstract theories into measurable forms by rigorously defining variables, selecting appropriate indicators, and deriving non-causal assumptions. The utility of the framework is demonstrated through its application to a theory on DevOps team classification. The resulting methodology provides researchers with a robust foundation for conducting verifiable theoretical studies while simultaneously offering practitioners actionable, theory-informed insights.

Abstract

Software Engineering often adapts theory-building frameworks from the social sciences to address socio-technical complexity. The key phases of the theory-building process are conceptual development, operationalization, testing, and application. Operationalization translates abstract concepts into measurable elements for empirical validation. This phase is essential for delivering the practical utility required by an applied science like Software Engineering. We propose a systematic procedure for the operationalization phase that bridges the gap between abstract concepts and empirical validation, ensuring the resulting theory is both rigorous and practically useful. We extend the operationalization framework proposed by Sjøberg et al. and formulate non-causal hypotheses following Dubin's approach. Our procedure defines variables, selects indicators, and systematically derives hypotheses. We present a replicable, evidence-based methodological guideline that preserves a clear chain of evidence and supports practical validation. We illustrate the procedure using the DevOps Team Taxonomies Theory. This guideline provides a transparent chain of evidence from theory to testable elements, empowering researchers to ground theoretical advancements in empirical evidence and deliver actionable insights for practitioners.

Problem

• operationalization
• software engineering theory
• empirical validation
• theory-building
• practical utility

Innovation

• operationalization
• theory-building
• empirical validation
• non-causal hypotheses
• evidence-based methodology