Browsing by Author "Chaudron, Michel R.V."

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    Comparison of Occurrence of Design Smells in Desktop and Mobile Applications
    (ACSE, 2020) Ogenrwot, Daniel; Nakatumba-Nabende, Joyce; Chaudron, Michel R.V.
    Design smells are symptoms of poor solutions to recurring design problems in a software system. Those symptoms have a direct negative impact on software quality by making it difficult to comprehend and maintain. In this paper we compare the occurrence of design smells between different technological ecosystems: windows/desktop and android/mobile. This knowledge is significant for various software maintenance activities such as program quality assurance and refactoring. To supplement previous findings, our study aimed at (a) understanding if and how the relationship among design smells differs across windows and mobile applications and (b) determining the groups of design smells that tend to occur frequently together and the magnitude of their occurrence in windows and mobile applications. In this study, we explored the use of statistics and unsupervised learning on a dataset consisting of twelve (12) Javabased open-source projects mined from GitHub. We identified fifteen (15) most frequent design smells across desktop and mobile applications. Additionally, a clustering technique revealed which groups of design smells that often co-occur. Specifically, {SpeculativeGenerality, SwissArmyKnife} and {LongParameterList, ClassDataShouldBePrivate} are observed to occur frequently together in desktop and mobile applications.
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    Integration of design smells and role-stereotypes classification dataset
    (Data in Brief, 2021) Ogenrwot, Daniel; Nakatumba-Nabende, Joyce; Chaudron, Michel R.V.
    Design smells are recurring patterns of poorly designed (fragments of) software systems that may hinder main- tainability. 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 mainte- nance of software systems. To improve software design and maintainability, there is a need to understand the relation- ship between design smells and role stereotypes. This pa- per presents a fine-grained dataset of systematically inte- grated design smells detection and role-stereotypes classi- fication 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 algo- rithm. 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 understand- ing the non-innate relationship between design smells and role-stereotypes.