Behavioral Maps: Identifying Architectural Smells in Self-Adaptive Systems at Runtime

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Self-adaptive systems (SAS) change their behavior and structure at runtime, depending on environmental changes and reconfiguration plans and goals. Such systems combine architectural fragments or solutions in their (re)configuration process. However, this process may negatively impact the system's architectural qualities, exhibiting architectural bad smells (ABS). Also, some smells may appear in only particular runtime conditions. This issue is challenging to detect due to the combinatorial explosion of interactions amongst features. We initially proposed the notion of Behavioral Map to explore architectural issues at runtime. This extended study applies the Behavioral Map to analyze the ABS in self-adaptive systems at runtime. In particular, we look for Cyclic Dependency, Extraneous Connector, Hub-Like Dependency, and Oppressed Monitor ABS in various runtime adaptations in the Smart Home Environment (SHE) framework, Adasim, and mRUBiS systems developed in Java. The results indicate that runtime ABS identification is required to fully capture SAS architectural qualities because the ABS are feature-dependent, and their number is highly variable for each adaptation. We have observed that some ABS appears in all runtime adaptations, some in only a few. However, some ABS only appear in the publish-subscribe architecture, such as Extraneous Connector and Oppressed Monitor smell. We discuss the reasons behind these architectural smells for each system and motivate the need for targeted ABS analyses in SAS.
langue originaleAnglais
titreSoftware Architecture - 15th European Conference, ECSA 2021 Tracks and Workshops, Revised Selected Papers
Sous-titre15th European Conference, ECSA 2021 Tracks and Workshops; Växjö, Sweden, September 13–17, 2021, Revised Selected Papers
rédacteurs en chefPatrizia Scandurra, Matthias Galster, Raffaela Mirandola, Danny Weyns, Danny Weyns
EditeurSpringer Nature Switzerland AG
Nombre de pages22
EditionLecture Notes in Computer Science
ISBN (Electronique)978-3-031-15116-3
ISBN (imprimé)978-3-031-15116-3, 978-3-031-15115-6
Les DOIs
Etat de la publicationPublié - 19 août 2022

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13365 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

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