论文标题
在用例描述中发现不良气味
Detecting Bad Smells in Use Case Descriptions
论文作者
论文摘要
用例建模非常受欢迎，可以表示要开发的系统的功能，并且由两个部分组成：用例图和用例描述。用例描述以结构化的自然语言（NL）书写，NL的使用可能导致诸如模棱两可，不一致和/或不完整的描述等不良描述。较差的描述导致缺失的要求，引起了不正确的要求，以及生产的用例模型的理解性较低。本文提出了一种自动检测用例描述的不良气味，描述不良的症状的技术。首先，为了澄清难闻的气味，我们分析了现有的用例模型，以具体地发现了不良用例描述，并制定了不良气味的清单，即不良气味的目录。一些不良气味可以使用目标问题范式将其自动化为自动化的措施。本文的主要贡献是对不良气味的自动检测。首先，我们已经实现了22种不良气味的自动气味探测器，并通过实验评估了其有用性。结果，我们的工具的第一个版本的精度比为0.591，召回率为0.981。
Use case modeling is very popular to represent the functionality of the system to be developed, and it consists of two parts: use case diagram and use case description. Use case descriptions are written in structured natural language (NL), and the usage of NL can lead to poor descriptions such as ambiguous, inconsistent and/or incomplete descriptions, etc. Poor descriptions lead to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced use case models. This paper proposes a technique to automate detecting bad smells of use case descriptions, symptoms of poor descriptions. At first, to clarify bad smells, we analyzed existing use case models to discover poor use case descriptions concretely and developed the list of bad smells, i.e., a catalogue of bad smells. Some of the bad smells can be refined into measures using the Goal-Question-Metric paradigm to automate their detection. The main contribution of this paper is the automated detection of bad smells. We have implemented an automated smell detector for 22 bad smells at first and assessed its usefulness by an experiment. As a result, the first version of our tool got a precision ratio of 0.591 and recall ratio of 0.981.
