Background: Requirements engineering is of a principal importance when starting a new project. However, the number of the requirements involved in a single project can reach up to thousands. Controlling and assuring the quality of natural language requirements (NLRs), in these quantities, is challenging. Aims: In a field study, we investigated with the Swedish Transportation Agency (STA) to what extent the characteristics of requirements had an influence on change requests and budget changes in the project. Method: We choose the following models to characterize system requirements formulated in natural language: Concern-based Model of Requirements (CMR), Requirements Abstractions Model (RAM) and Software-Hardware model (SHM). The classification of the NLRs was conducted by the three authors. The robust statistical measure Fleiss' Kappa was used to verify the reliability of the results. We used descriptive statistics, contingency tables, results from the Chi-Square test of association along with post hoc tests. Finally, a multivariate statistical technique, Correspondence analysis was used in order to provide a means of displaying a set of requirements in two-dimensional graphical form. Results: The results showed that software requirements are associated with less budget cost than hardware requirements. Moreover, software requirements tend to stay open for a longer period indicating that they are "harder" to handle. Finally, the more discussion or interaction on a change request can lower the actual estimated change request cost. Conclusions: The results lead us to a need to further investigate the reasons why the software requirements are treated differently from the hardware requirements, interview the project managers, understand better the way those requirements are formulated and propose effective ways of Software management.

翻译：背景：需求工程在启动新项目时具有首要重要性。然而，单个项目涉及的需求数量可达数千条。在这些数量级下，控制和确保自然语言需求（NLRs）的质量具有挑战性。目标：在一项实地研究中，我们与瑞典交通管理局（STA）合作，调查了需求特性在多大程度上影响项目中的变更请求和预算变化。方法：我们选择以下模型来表征以自然语言描述的系统需求：基于关注点的需求模型（CMR）、需求抽象模型（RAM）和软硬件模型（SHM）。NLRs的分类由三位作者共同完成。使用稳健的统计度量Fleiss' Kappa来验证结果的可靠性。我们采用了描述性统计、列联表、关联性卡方检验及其事后检验。最后，使用多变量统计技术——对应分析，以在二维图形形式下展示一组需求。结果：结果表明，软件需求相比硬件需求与更低的预算成本相关。此外，软件需求往往在更长时间内保持未解决状态，表明它们“更难”处理。最后，变更请求中更多的讨论或互动可以降低实际估算的变更请求成本。结论：结果引导我们有必要进一步研究为何软件需求与硬件需求被区别对待的原因，采访项目经理，更好地理解这些需求被表述的方式，并提出有效的软件管理方法。