The quality of requirements specifications may impact subsequent, dependent software engineering (SE) activities. However, empirical evidence of this impact remains scarce and too often superficial as studies abstract from the phenomena under investigation too much. Two of these abstractions are caused by the lack of frameworks for causal inference and frequentist methods which reduce complex data to binary results. In this study, we aim to demonstrate (1) the use of a causal framework and (2) contrast frequentist methods with more sophisticated Bayesian statistics for causal inference. To this end, we reanalyze the only known controlled experiment investigating the impact of passive voice on the subsequent activity of domain modeling. We follow a framework for statistical causal inference and employ Bayesian data analysis methods to re-investigate the hypotheses of the original study. Our results reveal that the effects observed by the original authors turned out to be much less significant than previously assumed. This study supports the recent call to action in SE research to adopt Bayesian data analysis, including causal frameworks and Bayesian statistics, for more sophisticated causal inference.

翻译：需求规格说明的质量可能会影响后续的、依赖性的软件工程活动。然而，关于这种影响的实证证据仍然稀缺，且往往过于表面化，因为研究过度抽象了所调查的现象。造成这两种抽象的原因在于缺乏因果推理框架以及将复杂数据简化为二元结果的频率主义方法。在本研究中，我们旨在展示：(1) 因果框架的应用，以及 (2) 将频率主义方法与更复杂的贝叶斯统计方法进行对比以进行因果推理。为此，我们重新分析了唯一一项已知的、调查被动语态对后续领域建模活动影响的对照实验。我们遵循统计因果推理框架，并采用贝叶斯数据分析方法重新审视原始研究的假设。我们的结果表明，原始作者观察到的效应实际上远不如之前认为的那么显著。本研究支持了软件工程研究中近期关于采用贝叶斯数据分析（包括因果框架和贝叶斯统计）以实现更复杂因果推理的行动呼吁。