DescriptionDynamic Software Product Lines (DSPLs) engineering implements self-adaptive systems by dynamically binding or unbinding features at runtime according to a feature model. However, these features may interact in unexpected and undesired ways leading to critical consequences for the DSPL. Moreover, (re)configurations may negatively affect the runtime system's architectural qualities, manifesting architectural bad smells. These issues are challenging to detect due to the combinatorial explosion of the number of interactions amongst features. As some of them may appear at runtime, we need a runtime approach to their analysis and mitigation. This thesis introduces the Behavioral Map (BM) formalism that captures information from different sources (feature model, code) to automatically detect these issues. We provide behavioral map inference algorithms. Using the Smart Home Environment (SHE) as a case study, we describe how a BM is helpful to identify critical feature interactions and architectural smells. Our preliminary results already show promising progress for both feature interactions and architectural bad smells identification at runtime.
|Période||6 sept. 2021|
|Titre de l'événement||25th ACM International Systems and Software Product Line Conference - Workshops|
|Type d'événement||Une conférence|
|Numéro de conférence||25|
|Niveau de reconnaissance||International|
Documents et liens
Activité: Types de Participation ou d'organisation d'un événement › Participation à une conférence, un congrès