Architectural Bad Smells for Self-Adaptive Systems: Go Runtime!

Edilton Lima dos Santos; Pierre-Yves Schobbens; Ivan Machado; Gilles Perrouin

doi:10.1145/3571788.3571802

Architectural Bad Smells for Self-Adaptive Systems: Go Runtime!

Edilton Lima dos Santos, Pierre-Yves Schobbens, Ivan Machado, Gilles Perrouin

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Résumé

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.

langue originale	Anglais
titre	Proceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2023, Odense, Denmark, January 25-27, 2023
Sous-titre	17th International Working Conference on Variability Modelling of Software-Intensive Systems
rédacteurs en chef	Myra B. Cohen, Thomas Thüm, Jacopo Mauro
Editeur	ACM Press
Pages	85-87
Nombre de pages	3
ISBN (Electronique)	9798400700019
Les DOIs	https://doi.org/10.1145/3571788.3571802
Etat de la publication	Publié - 25 janv. 2023

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Nom	ACM International Conference Proceeding Series

SDG des Nations Unies

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10.1145/3571788.3571802

VaMoS2023-ABS-GO-RUNTIMEAccepted author manuscript, 471 KBLicense: autre

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1 Participation à une conférence, un congrès
1 Présentation orale

Architectural Bad Smells for Self-Adaptive Systems: Go Runtime!
Lima dos Santos, E. (Orateur)
26 janv. 2023
Activité: Discours ou présentation › Présentation orale
17th International Working Conference on Variability Modelling of Software-Intensive Systems
Lima dos Santos, E. (Orateur)
25 janv. 2023 → 27 janv. 2023
Activité: Participation ou organisation d'un événement › Participation à une conférence, un congrès

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. 2023
Student thesis: Doc types › Docteur en Sciences

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Santos, E. L. D., Schobbens, P.-Y., Machado, I., & Perrouin, G. (2023). Architectural Bad Smells for Self-Adaptive Systems: Go Runtime! Dans 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 (p. 85-87). (ACM International Conference Proceeding Series). ACM Press. https://doi.org/10.1145/3571788.3571802

Santos, Edilton Lima dos ; Schobbens, Pierre-Yves ; Machado, Ivan et al. / Architectural Bad Smells for Self-Adaptive Systems : Go Runtime!. 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. Editeur / Myra B. Cohen ; Thomas Thüm ; Jacopo Mauro. ACM Press, 2023. p. 85-87 (ACM International Conference Proceeding Series).

@inproceedings{f61a207f435e4f2fb90795d804ed5537,

title = "Architectural Bad Smells for Self-Adaptive Systems: Go Runtime!",

abstract = "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{\textquoteright}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.",

keywords = "Architectural Quality, Architectural Smells, Runtime Validation., Self-Adaptive Systems, Software architecture",

author = "Santos, {Edilton Lima dos} and Pierre-Yves Schobbens and Ivan Machado and Gilles Perrouin",

note = "Funding Information: Edilton Lima dos Santos is funded by a CERUNA grant from the University of Namur. Gilles Perrouin is an FNRS Research Associate. This work was partly funded by the ERDF IDEES Co-innovation project, and partly funded by the Coordena{\c c}{\~a}o de Aperfei{\c c}oamento de Pessoal de N{\'i}vel Superior - Brasil (CAPES) - Finance Code 001. Publisher Copyright: {\textcopyright} 2023 ACM.",

year = "2023",

month = jan,

day = "25",

doi = "10.1145/3571788.3571802",

language = "English",

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Santos, ELD , Schobbens, P-Y, Machado, I & Perrouin, G 2023, Architectural Bad Smells for Self-Adaptive Systems: Go Runtime! Dans MB 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, p. 85-87. https://doi.org/10.1145/3571788.3571802

Architectural Bad Smells for Self-Adaptive Systems: Go Runtime! / Santos, Edilton Lima dos ; Schobbens, Pierre-Yves; Machado, Ivan et al.
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. Ed. / Myra B. Cohen; Thomas Thüm; Jacopo Mauro. ACM Press, 2023. p. 85-87 (ACM International Conference Proceeding Series).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

TY - GEN

T1 - Architectural Bad Smells for Self-Adaptive Systems

T2 - Go Runtime!

AU - Santos, Edilton Lima dos

AU - Schobbens, Pierre-Yves

AU - Machado, Ivan

AU - Perrouin, Gilles

N1 - Funding Information: Edilton Lima dos Santos is funded by a CERUNA grant from the University of Namur. Gilles Perrouin is an FNRS Research Associate. This work was partly funded by the ERDF IDEES Co-innovation project, and partly funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Publisher Copyright: © 2023 ACM.

PY - 2023/1/25

Y1 - 2023/1/25

N2 - 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.

AB - 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.

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KW - Runtime Validation.

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M3 - Conference contribution

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SP - 85

EP - 87

BT - Proceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2023, Odense, Denmark, January 25-27, 2023

A2 - Cohen, Myra B.

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PB - ACM Press

ER -

Santos ELD , Schobbens PY, Machado I, Perrouin G. Architectural Bad Smells for Self-Adaptive Systems: Go Runtime! Dans Cohen MB, Thüm T, Mauro J, rédacteurs en chef, 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 Press. 2023. p. 85-87. (ACM International Conference Proceeding Series). doi: 10.1145/3571788.3571802

Architectural Bad Smells for Self-Adaptive Systems: Go Runtime!

Résumé

Série de publications

SDG des Nations Unies

Accès au document

Autres fichiers et liens

Empreinte digitale

Activités

Architectural Bad Smells for Self-Adaptive Systems: Go Runtime!

17th International Working Conference on Variability Modelling of Software-Intensive Systems

Thèses de l'étudiant

Behavioral Maps: A Framework to Assess and Validate Self-Adaptive Architectures at Runtime

Contient cette citation