conduct external validation

Experimental reproducibility in Empirical Software Engineering (ESE) is hindered by a fundamental disconnect between idealized methodological assumptions—e.g., standardized protocols and controlled conditions—and researchers’ actual experimental practices. Method: We conducted a two-year ethnographic study involving participant observation, in-depth interviews, and content analysis of experimental artifacts across diverse ESE research teams. Contribution/Results: We identify four critical dimensions—activity diversity, role distribution, conceptual granularity, and domain perspective—in which real-world experimentation systematically deviates from textbook models. Based on these findings, we propose the first high-fidelity conceptual and process model grounded in empirical research practice, explicitly capturing the “practice gap” underlying irreproducibility. This model provides foundational evidence and design principles for developing next-generation reproducibility-support tools, methodological guidelines, and evaluation frameworks in ESE.

Preclinical reproducibility assessment traditionally relies on costly additional replicate experiments, limiting scalability and efficiency. Method: This study proposes leveraging inherent internal replication—such as across batches, sites, and litters—as a quantifiable resource for reproducibility evaluation. We systematically define six classes of internal replication structures and develop a statistical inference framework integrating mixed-effects modeling, variance decomposition, and multi-site collaborative analysis, augmented by a formal reproducibility hypothesis test. Contribution/Results: Validated on a three-center mouse study, the method significantly enhances statistical robustness and inferential reliability without requiring new experiments. It delivers an immediately deployable, data-driven tool for preclinical reproducibility assessment, enabling a paradigm shift from experiment-driven to data-driven reproducibility evaluation.

The AI/ML community faces a severe reproducibility crisis, primarily driven by conceptual ambiguity in verification terminology—such as “reproducibility,” “replicability,” and “dependency/independence”—which undermines research credibility and scientific progress. To address this, we propose the first five-dimensional verification taxonomy, systematically defining core concepts—including reproducibility, dependency vs. independent re-executability, and direct vs. conceptual replicability—by clarifying their objectives, prerequisites, and evaluation criteria. Our framework integrates conceptual analysis, terminological standardization, and methodological modeling to yield a structured verification guideline. It enhances experimental rigor in study design, fosters consensus across the research community on verification practices, and significantly improves cross-team result reproducibility and outcome reliability.

This study addresses the challenge of integrating randomized or single-arm clinical trials with external experimental or observational data to enable cross-study treatment comparisons and improve estimation precision of treatment effects. Methodologically, building upon the potential outcomes framework, we first develop a unified identification strategy for hybrid-data designs, systematically characterizing identifiability conditions across diverse designs—including historical controls, synthetic controls, and anchoring estimators—and propose a generalizable taxonomy of such designs along with corresponding causal inference principles. Our contribution lies in filling a critical theoretical gap in regulatory science regarding the rigorous integration of external controls, thereby establishing a methodological foundation for leveraging real-world evidence to complement trial-based evidence in pharmaceutical and medical device evaluation. This advancement significantly enhances the transportability of evidence and its applicability to regulatory decision-making.

Although top-tier conferences such as ICSE now commonly require authors to submit replication packages, the actual executability and reproducibility of these packages remain largely unassessed. This study presents a large-scale empirical investigation of 100 replication packages from ICSE papers published between 2015 and 2024, involving approximately 650 person-hours of manual execution, debugging, and root-cause analysis. The findings reveal that only 40% of the packages are executable, with just 32.5% running without modification; 82.5% require moderate to substantial changes. Among the executable packages, merely 35% successfully reproduce the original results. This work is the first to expose a significant gap between executability and reproducibility in software engineering replication packages and proposes three actionable guidelines to improve their reliability and utility.

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.