AI 中文总结
针对软件工程中经验证据管理不足的问题，提出基于方法工程的研究综合框架，系统关联复制、修订与再分析三类证据演化，以提升外部、内部与结论效度，并确定领域前沿。
AI 中文摘要
定量经验研究的一个理想成果是方差理论，即对自变量对因变量影响的量化。方差理论的有效性源于对多个证据的综合，这使其有效性超越了单一研究的发现。然而，软件工程中的研究综合很少见，且如果存在，通常仅限于纯粹的叙述性综合。至多，研究者会进行元分析，以综合来自多个定量结果的方差理论。但即便是元分析，也只有在综合精确复制时才能产生可靠结果，却无法从变体中推广。我们旨在将研究综合的前沿扩展到当前技术水平之上，以系统化管理经验证据及其演化。我们应用方法工程，从经过验证的单个方法片段中构建一个用于研究综合的框架。该框架允许研究者将新证据与现有证据体系明确关联，并系统性地扩展关于所研究现象的知识。我们通过显式建模现有证据片段之间的关系，展示了该框架在两个研究领域的应用。该框架将三种证据演化类型关联起来：(1) 复制（replications）在新情境中检验相同假设以提高外部效度，(2) 修订（revisions）挑战现有假设以提高内部效度，(3) 再分析（reanalyses）替换分析方法以提高结论效度。通过系统性的证据演化以及对每个效度维度的清晰评估标准，所提出的框架能够确定研究领域的前沿。该框架提供了一种视角，用于系统性地演化软件工程中的经验证据，支持在该领域取得更具建设性和生产性的进展。
英文摘要
One aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. The validity of variance theories stems from the synthesis of multiple pieces of evidence, which increases its validity beyond the findings of a single study. However, research synthesis in SE is rare and if done mostly limited to purely narrative syntheses. At best, researchers perform meta-analyses to synthesize variance theories from several quantitative results. But even meta-analyses only produce reliable results when synthesizing exact replications yet fail to generalize from variations. We aim to extend the frontier of research synthesis beyond the state-of-the-art to systematically manage empirical evidence and its evolution. We apply method engineering to construct a framework for research synthesis from proven, individual method fragments. The framework allows researchers to put new evidence in a clear relation to an existing body of evidence and systematically expand knowledge about a studied phenomenon. We demonstrate the application of this framework to two fields of research by explicitly modeling the relationship between existing pieces of evidence. The framework puts three types of evolution of evidence into relation: (1) replications investigate the same hypothesis in a new context to improve external validity, (2) revisions challenge an existing hypothesis to improve internal validity, and (3) reanalyses replace analysis methods to improve conclusion validity. Through a systematic evolution of evidence and clear assessment criteria for each dimension of validity, the proposed framework can determine the frontier of a field of research. The framework provides a perspective to systematically evolve empirical evidence in SE, supporting more constructive and productive advances in our field.
DOI：10.3389/fcomp.2026.1937190
