The growth of the video game market, the large proportion of games targeting mobile devices or streaming services, and the increasing complexity of video games trigger the availability of video game-specific tools to assess performance and maintainability problems. This paper proposes UnityLinter, a static analysis tool that supports Unity video game developers to detect seven types of bad smells we have identified as relevant in video game development. Such smell types pertain to performance, maintainability and incorrect behavior problems. After having defined the smells by analyzing the existing literature and discussion forums, we have assessed their relevance with a survey involving 68 participants. Then, we have analyzed the occurrence of the studied smells in 100 open-source Unity projects, and also assessed UnityLinter's accuracy. Results of our empirical investigation indicate that developers well-received performance-and behavior-related issues, while some maintainability issues are more controversial. UnityLinter is, in general, accurate enough in detecting smells (86%-100% precision and 50%-100% recall), and our study shows that the studied smell types occur in 39%-97% of the analyzed projects.
Detecting Video Game-Specific Bad Smells in Unity Projects
Borrelli A.;Nardone V.;Di Lucca G. A.;Canfora G.;Di Penta M.
2020-01-01
Abstract
The growth of the video game market, the large proportion of games targeting mobile devices or streaming services, and the increasing complexity of video games trigger the availability of video game-specific tools to assess performance and maintainability problems. This paper proposes UnityLinter, a static analysis tool that supports Unity video game developers to detect seven types of bad smells we have identified as relevant in video game development. Such smell types pertain to performance, maintainability and incorrect behavior problems. After having defined the smells by analyzing the existing literature and discussion forums, we have assessed their relevance with a survey involving 68 participants. Then, we have analyzed the occurrence of the studied smells in 100 open-source Unity projects, and also assessed UnityLinter's accuracy. Results of our empirical investigation indicate that developers well-received performance-and behavior-related issues, while some maintainability issues are more controversial. UnityLinter is, in general, accurate enough in detecting smells (86%-100% precision and 50%-100% recall), and our study shows that the studied smell types occur in 39%-97% of the analyzed projects.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
