Requirement specifications are typically written in natural language (NL) due to its usability across multiple domains and understandability by all stakeholders. However, unstructured NL is prone to quality problems (e.g., ambiguity) when writing requirements, which can result in project failures. To address this issue, we present a tool, named Paska, that takes as input any NL requirements, automatically detects quality problems as smells in the requirements, and offers recommendations to improve their quality. Our approach relies on natural language processing (NLP) techniques and a state-of-the-art controlled natural language (CNL) for requirements (Rimay), to detect smells and suggest recommendations using patterns defined in Rimay to improve requirement quality. We evaluated Paska through an industrial case study in the financial domain involving 13 systems and 2725 annotated requirements. The results show that our tool is accurate in detecting smells (89% precision and recall) and suggesting appropriate Rimay pattern recommendations (96% precision and 94% recall).

中文翻译：

---

自然语言要求中的自动气味检测和推荐

需求规范通常用自然语言 (NL) 编写，因为它具有跨多个领域的可用性并且易于所有利益相关者理解。然而，非结构化的NL在编写需求时容易出现质量问题（例如模糊性），从而导致项目失败。为了解决这个问题，我们提出了一个名为 Paska 的工具，它将任何 NL 需求作为输入，自动检测需求中的质量问题，并提供提高质量的建议。我们的方法依赖于自然语言处理 (NLP) 技术和最先进的受控自然语言 (CNL) 需求 (Rimay)，以检测气味并使用 Rimay 中定义的模式提出建议，以提高需求质量。我们通过金融领域的行业案例研究对 Paska 进行了评估，涉及 13 个系统和 2725 个带注释的需求。结果表明，我们的工具可以准确地检测气味（89% 的精确度和召回率）并提出适当的 Rimay 模式建议（96% 的精确度和 94% 的召回率）。