Integration of design smells and role-stereotypes classification dataset

• Main Authors: Daniel Ogenrwot, Joyce Nakatumba-Nabende, Michel R.V. Chaudron
• Format: Article
• Language: English
• Published: Elsevier 2021-06-01
• Series: Data in Brief
• Subjects: Software design; Role-stereotype; Design smells; Software quality
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352340921004091

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.