demonstration

ArchLearner: leveraging machine-learning techniques for proactive architectural adaptation

Authors:

Henry Muccini,

Karthik VaidhyanathanAuthors Info & Claims

ECSA '19: Proceedings of the 13th European Conference on Software Architecture - Volume 2

Pages 38 - 41

https://doi.org/10.1145/3344948.3344962

Published: 09 September 2019 Publication History

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Abstract

Self-adaptation is nowadays considered as one of the possible solutions to handle the uncertainties faced by software at run-time. This is especially true in the case of IoT systems. These uncertainties can, in turn, affect the system QoS (Quality Of Service). In this tool demo, we present a machine learning driven proactive decision-making tool named ArchLearner, for aiding architectural adaptation. The tool enables the given IoT system to i) automatically identify the need for adaptation at an early stage; ii) perform automated decision making for generating the best adaptation strategy; iii) gather the feedback of the selected decision for continuous improvement. It also enables the architects/developers to i) visualize the adaptation process in near real-time; ii) specify the required configurations; iii) visualize the real-time QoS data.

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Cited By

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Amiri ANtentos EZdun UGeiger S(2023)Tool Support for Learning Architectural Guidance Models from Architectural Design Decision ModelsProceedings of the 28th European Conference on Pattern Languages of Programs10.1145/3628034.3628037(1-14)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1145/3628034.3628037
Shafiq SMashkoor AMayr-Dorn CEgyed A(2021)A Literature Review of Using Machine Learning in Software Development Life Cycle StagesIEEE Access10.1109/ACCESS.2021.31197469(140896-140920)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3119746

Index Terms

ArchLearner: leveraging machine-learning techniques for proactive architectural adaptation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Software architectures

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Information

Published In

ECSA '19: Proceedings of the 13th European Conference on Software Architecture - Volume 2

September 2019

286 pages

ISBN:9781450371421

DOI:10.1145/3344948

Editors:
Laurence Duchien
University of Lille, France
,
Catia Trubiani
Gran Sasso Science Institute (GSSI), Italy
,
Riccardo Scandariato
Chalmers | University of Gothenburg, Sweden
,
Raffaela Mirandola
Polytecnico di Milano, Italy
,
Elena Maria Navarro Martinez
Universidad de Castilla-La Mancha, Spain
,
Danny Weyns
Katholieke Universiteit Leuven, Belgium
,
Anne Koziolek
Karlsruhe Institute of Technology, Germany
,
Patrizia Scandurra
University of Bergamo, Italy
,
Clément Quinton
University of Lille, France

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2019

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Demonstration

Conference

ECSA

ECSA: European Conference on Software Architecture

September 9 - 13, 2019

Paris, France

Acceptance Rates

ECSA '19 Paper Acceptance Rate 48 of 72 submissions, 67%;

Overall Acceptance Rate 48 of 72 submissions, 67%

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Cited By

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Amiri ANtentos EZdun UGeiger S(2023)Tool Support for Learning Architectural Guidance Models from Architectural Design Decision ModelsProceedings of the 28th European Conference on Pattern Languages of Programs10.1145/3628034.3628037(1-14)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1145/3628034.3628037
Shafiq SMashkoor AMayr-Dorn CEgyed A(2021)A Literature Review of Using Machine Learning in Software Development Life Cycle StagesIEEE Access10.1109/ACCESS.2021.31197469(140896-140920)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3119746

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