The Replication Assessment Problem in Software Engineering

2026-07-15

AI Summary

This study addresses the ambiguity and inconsistency in evaluation criteria for software engineering replication studies, which have led to contradictory interpretations and uncertainty in reported results. Through a systematic review of ten replication studies published between 2021 and 2025, combined with qualitative content analysis, statistical principles, and modeling of measurement uncertainty, this work is the first to uncover the heterogeneity and lack of standardized practices in current evaluation approaches. Building on these insights, the paper proposes a unified evaluation framework that integrates statistical theory, methodological rigor, and measurement theory. Empirical illustration demonstrates that the framework effectively enhances the transparency, consistency, comparability, and reliability of replication studies in software engineering.

Abstract

Background: Replication studies in software engineering are increasingly common, yet their interpretation remains uncertain and inconsistent because assessments frequently rely on loosely defined or ad hoc criteria. Aim: This study aims to document how replication study outcomes are currently assessed in empirical software engineering, identify problems arising from inconsistent criteria and propose a principled framework for meaningful evaluation. Method: We conducted a systematic review of replication studies, with the search covering recent empirical software engineering replications (2021--2025). For each study, we extracted the criteria used to assess replication outcomes and analysed these for heterogeneity, logical consistency, and alignment with established statistical principles. Results: A total of 10 replication studies were located. The analysis reveals substantial heterogeneity in assessment practices, with contradictory criteria applied to similar data, limited acknowledgement of measurement uncertainty, and an absence of shared standards. We propose a principled framework grounded in statistical, methodological, and measurement considerations, and demonstrate its application through worked examples. Conclusions: Adopting consistent and transparent assessment principles would reduce ambiguity, improve comparability, and support more reliable evidence accumulation in software engineering replication research.

Problem

• Replication Assessment
• Software Engineering
• Empirical Studies
• Evaluation Criteria
• Research Reproducibility

Innovation

• replication assessment
• evaluation framework
• empirical software engineering
• statistical consistency
• measurement uncertainty