Article

Automated mining of software component interactions for self-adaptation

Authors:

Naeem Esfahani,

Sam MalekAuthors Info & Claims

SEAMS 2014: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

Pages 27 - 36

https://doi.org/10.1145/2593929.2593934

Published: 02 June 2014 Publication History

Abstract

A self-adaptive software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent "models@runtime" approaches usually require an a priori model for a system's dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system's behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. Our preliminary evaluation of the approach against an Emergency Deployment System shows that the associations mining model can be used to effectively address a variety of adaptation needs, including (1) safely applying dynamic changes to a running software system without creating inconsistencies, (2) identifying potentially malicious (abnormal) behavior for self-protection, and (3) our ongoing research on improving deployment of software components in a distributed setting for performance self-optimization.

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Cited By

Mertz JNunes I(2023)Software runtime monitoring with adaptive sampling rate to collect representative samples of execution tracesJournal of Systems and Software10.1016/j.jss.2023.111708202:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111708
Shatnawi ASeriai ASahraoui H(2022)Extraction of Software Product Line Architectures from Many System VariantsHandbook of Re-Engineering Software Intensive Systems into Software Product Lines10.1007/978-3-031-11686-5_8(199-220)Online publication date: 5-Jul-2022
https://doi.org/10.1007/978-3-031-11686-5_8
Mertz JNunes ILitoiu MClarke STei K(2019)On the practical feasibility of software monitoringProceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1109/SEAMS.2019.00030(169-180)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/SEAMS.2019.00030
Show More Cited By

Index Terms

Automated mining of software component interactions for self-adaptation
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Software architectures

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cover image ACM Conferences

SEAMS 2014: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

June 2014

174 pages

ISBN:9781450328647

DOI:10.1145/2593929

General Chair:
Gregor Engels
University of Paderborn, Germany
,
Program Chair:
Nelly Bencomo
Aston University, UK

Copyright © 2014 ACM.

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SIGSOFT: ACM Special Interest Group on Software Engineering

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TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

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Published: 02 June 2014

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ICSE '14

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ICSE '14: 36th International Conference on Software Engineering

June 2 - 3, 2014

Hyderabad, India

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Overall Acceptance Rate 17 of 31 submissions, 55%

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Cited By

Mertz JNunes I(2023)Software runtime monitoring with adaptive sampling rate to collect representative samples of execution tracesJournal of Systems and Software10.1016/j.jss.2023.111708202:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111708
Shatnawi ASeriai ASahraoui H(2022)Extraction of Software Product Line Architectures from Many System VariantsHandbook of Re-Engineering Software Intensive Systems into Software Product Lines10.1007/978-3-031-11686-5_8(199-220)Online publication date: 5-Jul-2022
https://doi.org/10.1007/978-3-031-11686-5_8
Mertz JNunes ILitoiu MClarke STei K(2019)On the practical feasibility of software monitoringProceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1109/SEAMS.2019.00030(169-180)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/SEAMS.2019.00030
Sadeghi AEsfahani NMalek S(2017)Ensuring the Consistency of Adaptation through Inter- and Intra-Component Dependency AnalysisACM Transactions on Software Engineering and Methodology10.1145/306338526:1(1-27)Online publication date: 10-May-2017
https://dl.acm.org/doi/10.1145/3063385
Gerostathopoulos IBures THnetynka PHujecek APlasil FSkoda D(2017)Strengthening Adaptation in Cyber-Physical Systems via Meta-Adaptation StrategiesACM Transactions on Cyber-Physical Systems10.1145/28233451:3(1-25)Online publication date: 4-Apr-2017
https://dl.acm.org/doi/10.1145/2823345
Malek SCanavera KEsfahani N(2017)Automated Inference Techniques to Assist With the Construction of Self-Adaptive SoftwareManaging Trade-Offs in Adaptable Software Architectures10.1016/B978-0-12-802855-1.00006-X(131-154)Online publication date: 2017
https://doi.org/10.1016/B978-0-12-802855-1.00006-X
Giese HVogel TDiaconescu AGötz SBencomo NGeihs KKounev SBellman K(2017)State of the Art in Architectures for Self-aware Computing SystemsSelf-Aware Computing Systems10.1007/978-3-319-47474-8_8(237-275)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-47474-8_8
Giese HVogel TDiaconescu AGötz SBellman K(2017)Generic Architectures for Individual Self-aware Computing SystemsSelf-Aware Computing Systems10.1007/978-3-319-47474-8_6(149-189)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-47474-8_6
Giese HVogel TDiaconescu AGötz SKounev S(2017)Architectural Concepts for Self-aware Computing SystemsSelf-Aware Computing Systems10.1007/978-3-319-47474-8_5(109-147)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-47474-8_5
Cámara JBellman KKephart JAutili MBencomo NDiaconescu AGiese HGötz SInverardi PKounev STivoli M(2017)Self-aware Computing Systems: Related Concepts and Research AreasSelf-Aware Computing Systems10.1007/978-3-319-47474-8_2(17-49)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-47474-8_2
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