应用贝叶斯数据分析进行需求质量因果推断：一项对照实验
Applying Bayesian Data Analysis for Causal Inference about Requirements Quality: A Controlled Experiment
AI总结：
本文通过25名参与者参与的对照实验，结合频率学派与贝叶斯数据分析，评估被动语态和歧义代词对领域模型的因果影响，发现二者危害显著不同。
摘要：
人们普遍认为，需求规格说明的质量会影响后续的软件工程活动。然而，我们仍然缺乏经验证据来帮助组织判断其需求是否足够好，或是否会妨碍后续活动。我们的目标是为需求质量缺陷对依赖该需求的软件工程活动所产生的影响提供经验证据。我们开展了一项对照实验，来自工业界和大学的25名参与者根据包含不同质量缺陷的四条自然语言需求生成领域模型。我们同时使用频率学派数据分析和贝叶斯数据分析来评估生成的模型。与预期相反，我们的结果表明，被动语态的使用对生成的领域模型仅有较小影响。然而，歧义代词的使用对所生成领域模型的多种属性表现出强烈影响。最值得注意的是，歧义代词会导致领域模型中出现错误的关联。尽管文献和频率学派方法都同样建议避免这两种缺陷，但贝叶斯数据分析表明，所研究的两种质量缺陷对软件工程活动的影响差异巨大，因此值得给予不同程度的关注。研究人员可以进一步利用我们采用的方法，以改进关于需求质量的可靠、详细的经验证据。
英文摘要：
It is commonly accepted that the quality of requirements specifications impacts subsequent software engineering activities. However, we still lack empirical evidence to support organizations in deciding whether their requirements are good enough or impede subsequent activities. We aim to contribute empirical evidence to the effect that requirements quality defects have on a software engineering activity that depends on this requirement. We conduct a controlled experiment in which 25 participants from industry and university generate domain models from four natural language requirements containing different quality defects. We evaluate the resulting models using both frequentist and Bayesian data analysis. Contrary to our expectations, our results show that the use of passive voice only has a minor impact on the resulting domain models. The use of ambiguous pronouns, however, shows a strong effect on various properties of the resulting domain models. Most notably, ambiguous pronouns lead to incorrect associations in domain models. Despite being equally advised against by literature and frequentist methods, the Bayesian data analysis shows that the two investigated quality defects have vastly different impacts on software engineering activities and, hence, deserve different levels of attention. Our employed method can be further utilized by researchers to improve reliable, detailed empirical evidence on requirements quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
