Context: There are numerous textual notations and techniques that can be used in requirements engineering. Currently, practitioners make a choice without having scientific evidence regarding their suitability for given tasks. This uninformed choice may affect task performance. Objective: In this research, we investigate the adequacy of two well-known notations: use cases and user stories, as a starting point for the manual derivation of a structural conceptual model that represents the domain of the system. We also examine other factors that may affect the performance of this task. Methods: This work relies on two experiments. The first is a controlled classroom experiment. The second one is a quasi-experiment, conducted over multiple weeks, that aims at evaluating the quality of the derived conceptual model in light of the notation used, the adopted derivation process, and the complexity of the system to be. We measure quality in terms of validity and completeness of the conceptual model. Results: The results of the controlled experiment indicate that, for deriving conceptual models, user stories fit better than use cases. Yet, the second experiment indicates that the quality of the derived conceptual models is affected mainly by the derivation process and by the complexity of the case rather than the notation used. Contribution: We present evidence that the task of deriving a conceptual model is affected significantly by additional factors other than requirements notations. Furthermore, we propose implications and hypotheses that pave the way for further studies that compare alternative notations for the same task as well as for other tasks. Practitioners may use our findings to analyze the factors that affect the quality of the conceptual model when choosing a requirements notation and an elicitation technique that best fit their needs.

上下文：需求工程中可以使用许多文本符号和技术。当前，从业者在没有科学证据证明他们是否适合给定任务的情况下做出选择。这种无知的选择可能会影响任务性能。目的：在这项研究中，我们调查了两种众所周知的符号（用例和用户案例）的适当性，以此作为人工推导代表系统域的结构概念模型的起点。我们还将检查可能影响此任务性能的其他因素。方法：这项工作依赖于两个实验。第一个是受控的课堂实验。第二个是一个为期数周的准实验，旨在根据所使用的符号，所采用的推导过程以及所要使用的系统的复杂性来评估所推导的概念模型的质量。我们根据概念模型的有效性和完整性来衡量质量。结果：受控实验的结果表明，对于派生概念模型而言，用户故事比用例更适合。然而，第二个实验表明，派生的概念模型的质量主要受推导过程和案例复杂性的影响，而不是所用符号的影响。贡献：我们提供的证据表明，推导出概念模型的任务会受到除需求表示法以外的其他因素的显着影响。此外，我们提出了涵义和假设，为进一步的研究铺平了道路，这些研究将比较同一任务和其他任务的替代符号。从业人员可以使用我们的发现来分析影响概念模型质量的因素，从而选择最适合其需求的需求表示法和启发技术。