Ogenrwot Daniel, Nakatumba-Nabende Joyce, Chaudron Michel R V
Department of Computer Science, Gulu University, Uganda.
Department of Computer Science, Makerere University, Uganda.
Data Brief. 2021 May 8;36:107125. doi: 10.1016/j.dib.2021.107125. eCollection 2021 Jun.
Design smells are recurring patterns of poorly designed (fragments of) software systems that may hinder maintainability. Role-stereotypes indicate generic responsibilities that classes play in system design. Although the concepts of role-stereotypes and design smells are widely divergent, both are significant contributors to the design and maintenance of software systems. To improve software design and maintainability, there is a need to understand the relationship between design smells and role stereotypes. This paper presents a fine-grained dataset of systematically integrated design smells detection and role-stereotypes classification data. The dataset was created from a collection of twelve (12) real-life open-source Java projects mined from GitHub. The dataset consists of 18 design smells columns and 2,513 Java classes (rows) classified into six (6) role-stereotypes taxonomy. We also clustered the dataset into ten (10) different clusters using an unsupervised learning algorithm. Those clusters are useful for understanding the groups of design smells that often co-occur in a particular role-stereotype category. The dataset is significant for understanding the non-innate relationship between design smells and role-stereotypes.
设计异味是软件系统设计不佳（的片段）中反复出现的模式，可能会阻碍可维护性。角色刻板印象表示类在系统设计中所扮演的一般职责。尽管角色刻板印象和设计异味的概念大相径庭，但两者都是软件系统设计和维护的重要因素。为了改进软件设计和可维护性，有必要了解设计异味与角色刻板印象之间的关系。本文展示了一个经过系统集成的设计异味检测和角色刻板印象分类数据的细粒度数据集。该数据集是从从GitHub上挖掘的十二个（12个）实际开源Java项目的集合中创建的。该数据集由18个设计异味列和2513个Java类（行）组成，这些类被分类为六种（6种）角色刻板印象分类法。我们还使用无监督学习算法将数据集聚类为十个（10个）不同的簇。这些簇有助于理解在特定角色刻板印象类别中经常同时出现的设计异味组。该数据集对于理解设计异味与角色刻板印象之间的非固有关系具有重要意义。
