Architectural Bad Smells for Self-Adaptive Systems: Go Runtime!
| Autor: | Gilles Perrouin, Pierre Yves Schobbens, Ivan Machado, Edilton Lima dos Santos |
|---|---|
| Přispěvatelé: | Cohen, Myra B., Thüm, Thomas, Mauro, Jacopo |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Santos, E L D, Schobbens, P-Y, Machado, I & Perrouin, G 2023, Architectural Bad Smells for Self-Adaptive Systems : Go Runtime! in M B Cohen, T Thüm & J Mauro (eds), Proceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2023, Odense, Denmark, January 25-27, 2023 : 17th International Working Conference on Variability Modelling of Software-Intensive Systems . ACM International Conference Proceeding Series, ACM Press, pp. 85-87 . https://doi.org/10.1145/3571788.3571802 |
| DOI: | 10.1145/3571788.3571802 |
| Popis: | Self-adaptive systems (SAS) change their behavior and structure at runtime depending on environmental changes or user requests. For this purpose, the SASs combine architectural fragments or solutions in their adaptation process. However, this process may negatively impact the system’s architectural qualities, exhibiting architectural bad smells (ABS). Current studies perform ABS detection for SAS at design time, ignoring their intrinsic runtime variability. We demonstrate that this ignorance leads to inaccurate smell detections and possibly wrong maintenance decisions. We delineate the challenges runtime variability raise on ABS detection and argue that we should analyze SAS architectures at runtime. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|