Context

Requirements elicitation is a crucial phase in the software development life cycle. During requirements elicitation sessions, requirements engineers capture software requirements, and motivate stakeholders to express needs and expected software functionalities. In this context, there is a lack of extensive empirical research reporting the extent to which elicitation sessions can be influenced by participants' gender.

Objective

This paper presents our research endeavour to investigate requirements engineers' effort and elicited requirements' accuracy based on participants' gender.

Method

We conducted an experiment in two rounds with a total of 59 students who played the role of requirements engineers. In the first experimental task, the participant watched two videos where men and women stakeholders expressed software requirements. Later on, the participants specified software requirements in the shape of Business Process Model and Notation (BPMN) and next they generated Graphical User Interfaces (GUIs) from those models.

Results

We observed two significant differences. One between men and women requirements engineers in terms of dedicated effort during requirements specification: men took less effort. Other between stakeholders' gender in terms of accuracy resulted of BPMN models: models built from men stakeholders yield more accuracy. On the contrary, accuracy of resulted GUIs models did not show significant differences regarding requirements engineers or stakeholders' gender.

Conclusions

Analysing descriptive data, women spent more time both as stakeholders and as requirements engineers but their accuracy is better.

中文翻译：

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需求启发会议是否受参与者的性别影响？实证实验

语境

需求激发是软件开发生命周期中的关键阶段。在需求获取会议期间，需求工程师捕获软件需求，并激励利益相关者表达需求和预期的软件功能。在这种情况下，缺乏广泛的实证研究，报告了启发性会话受参与者性别影响的程度。

目的

本文介绍了我们的研究工作，以调查需求工程师的工作并根据参与者的性别得出需求的准确性。

方法

我们分两轮进行了实验，共有59名学生扮演了需求工程师的角色。在第一个实验任务中，参与者观看了两个视频，其中男女利益相关者表达了软件要求。后来，参与者以业务流程模型和符号（BPMN）的形式指定了软件需求，然后他们从这些模型生成了图形用户界面（GUI）。

结果

我们观察到两个显着差异。男人和女人的需求工程师之间在需求说明期间的专心致志：男人花的精力更少。BPMN模型导致了利益相关者性别之间的其他准确性：基于男性利益相关者建立的模型产生了更高的准确性。相反，所生成的GUI模型的准确性在需求工程师或利益相关者的性别方面并未显示出显着差异。

结论

通过分析描述性数据，女性在利益相关者和需求工程师方面花费的时间更多，但其准确性更高。