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short-paper

Learning to Adapt – Software Engineering for Uncertainty

Authors:

Deepali Kholkar,

Suman Roychoudhury,

Vinay Kulkarni,

Sreedhar ReddyAuthors Info & Claims

ISEC '22: Proceedings of the 15th Innovations in Software Engineering Conference

Article No.: 21, Pages 1 - 5

https://doi.org/10.1145/3511430.3511449

Published: 24 February 2022 Publication History

Abstract

Modern businesses are being subjected to an unprecedented variety of change drivers that cannot be predicted such as new regulations, emerging business models, and changing needs of stakeholders. This creates new demands on enterprises to meet stated goals in a dynamic and uncertain environment that translate to demands on the enterprise’s software systems. Software systems however are currently designed to deliver a fixed set of goals and assumed to operate in a static environment, falling short in addressing the need for continuous adaptation under uncertainty. State-of-the-art adaptation architectures like MAPE-K have been applied to meeting non-functional requirements in a dynamic environment using a static repository of knowledge. This paper articulates the need for architecting software systems that learn from their own operation to dynamically extend existing knowledge, and utilize the knowledge to meet stated functional goals in an uncertain environment.

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Cited By

Guinea-Cabrera MHolgado-Terriza J(2024)Digital Twins in Software Engineering—A Systematic Literature Review and VisionApplied Sciences10.3390/app1403097714:3(977)Online publication date: 23-Jan-2024
https://doi.org/10.3390/app14030977
Jézéquel JVallecillo A(2023)Uncertainty-aware Simulation of Adaptive SystemsACM Transactions on Modeling and Computer Simulation10.1145/358951733:3(1-19)Online publication date: 13-May-2023
https://dl.acm.org/doi/10.1145/3589517
Roychoudhury SSelukar MKholkar DSuraj Choudhary NKulkarni VReddy S(2023)Learning-Aided Adaptation - A Case Study from Wellness EcosystemEnterprise Design, Operations, and Computing. EDOC 2022 Workshops10.1007/978-3-031-26886-1_18(300-315)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-3-031-26886-1_18

Index Terms

Learning to Adapt – Software Engineering for Uncertainty
1. Computing methodologies
2. Software and its engineering

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Other conferences

ISEC '22: Proceedings of the 15th Innovations in Software Engineering Conference

February 2022

235 pages

ISBN:9781450396189

DOI:10.1145/3511430

Editors:
Saurabh Tiwari
DA-IICT Gandhinagar, India
,
Sanjay Chaudhary
Ahmedabad University, India
,
Chanchal K. Roy
University of Saskatchewan, Canada
,
Meenakshi D'Souza
IIIT Bangalore, India
,
Richa Sharma
Lock Haven University, USA
,
Lov Kumar
BITS-Pilani, Hyderabad, India

Copyright © 2022 ACM.

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Published: 24 February 2022

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ISEC 2022

ISEC 2022: 15th Innovations in Software Engineering Conference

February 24 - 26, 2022

Gandhinagar, India

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Cited By

Guinea-Cabrera MHolgado-Terriza J(2024)Digital Twins in Software Engineering—A Systematic Literature Review and VisionApplied Sciences10.3390/app1403097714:3(977)Online publication date: 23-Jan-2024
https://doi.org/10.3390/app14030977
Jézéquel JVallecillo A(2023)Uncertainty-aware Simulation of Adaptive SystemsACM Transactions on Modeling and Computer Simulation10.1145/358951733:3(1-19)Online publication date: 13-May-2023
https://dl.acm.org/doi/10.1145/3589517
Roychoudhury SSelukar MKholkar DSuraj Choudhary NKulkarni VReddy S(2023)Learning-Aided Adaptation - A Case Study from Wellness EcosystemEnterprise Design, Operations, and Computing. EDOC 2022 Workshops10.1007/978-3-031-26886-1_18(300-315)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-3-031-26886-1_18

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