Résumé
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 originale | Anglais |
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titre | Software Architecture - 15th European Conference, ECSA 2021 Tracks and Workshops, Revised Selected Papers |
Sous-titre | 15th European Conference, ECSA 2021 Tracks and Workshops; Växjö, Sweden, September 13–17, 2021, Revised Selected Papers |
rédacteurs en chef | Patrizia Scandurra, Matthias Galster, Raffaela Mirandola, Danny Weyns, Danny Weyns |
Editeur | Springer Nature Switzerland AG |
Pages | 159-180 |
Nombre de pages | 22 |
Volume | 13365 |
Edition | Lecture 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 publication | Publié - 19 août 2022 |
Série de publications
Nom | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13365 LNCS |
ISSN (imprimé) | 0302-9743 |
ISSN (Electronique) | 1611-3349 |
Empreinte digitale
Examiner les sujets de recherche de « Behavioral Maps: Identifying Architectural Smells in Self-Adaptive Systems at Runtime ». Ensemble, ils forment une empreinte digitale unique.Thèses de l'étudiant
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Behavioral Maps: A Framework to Assess and Validate Self-Adaptive Architectures at Runtime
Lima dos Santos, E. (Auteur)Perrouin, G. (Promoteur), Schobbens, P.-Y. (Promoteur), Remiche, M.-A. (Président), Englebert, V. (Jury), Mens, K. (Jury) & Raibulet, C. (Jury), 12 sept. 2023Student thesis: Doc types › Docteur en Sciences
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