Self-sustaining software systems (S4): Towards improved interpretability and adaptation

Christian Cabrera; Andrei Paleyes; Neil D. Lawrence

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Self-sustaining software systems (S4): Towards improved interpretability and adaptation

Christian Cabrera, Andrei Paleyes, Neil D. Lawrence

Proceedings of the 1st International Workshop on New Trends in Software Engineering, 2024.

Abstract

Software systems impact society at different levels as they pervasively solve real-world problems. Modern software systems are often so sophisticated that their complexity exceeds the limits of human comprehension. These systems must respond to changing goals, dynamic data, unexpected failures, and security threats, among other variable factors in real-world environments. Systems’ complexity challenges their interpretability and requires autonomous responses to dynamic changes. Two main research areas explore autonomous systems’ responses: evolutionary computing and autonomic computing. Evolutionary computing focuses on software improvement based on iterative modifications to the source code. Autonomic computing focuses on optimising systems’ performance by changing their structure, behaviour, or environment variables. Approaches from both areas rely on feedback loops that accumulate knowledge from the system interactions to inform autonomous decision-making. However, this knowledge is often limited, constraining the systems’ interpretability and adaptability. This paper proposes a new concept for interpretable and adaptable software systems: self-sustaining software systems (S4). S4 builds knowledge loops between all available knowledge sources that define modern software systems to improve their interpretability and adaptability. This paper introduces and discusses the S4 concept.

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BibTeX


@InProceedings{publications/self-sustaining-software-systems,
  title = 	 {Self-sustaining software systems (S4): Towards improved interpretability and adaptation},
  author = 	 {Cabrera, Christian and Paleyes, Andrei and Lawrence, Neil D.},
  booktitle = 	 {Proceedings of the 1st International Workshop on New Trends in Software Engineering},
  year = 	 {2024},
  url = 	 {/publications/self-sustaining-software-systems.html},
  abstract = 	 {Software systems impact society at different levels as they pervasively solve real-world problems. Modern software systems are often so sophisticated that their complexity exceeds the limits of human comprehension. These systems must respond to changing goals, dynamic data, unexpected failures, and security threats, among other variable factors in real-world environments. Systems' complexity challenges their interpretability and requires autonomous responses to dynamic changes. Two main research areas explore autonomous systems' responses: evolutionary computing and autonomic computing. Evolutionary computing focuses on software improvement based on iterative modifications to the source code. Autonomic computing focuses on optimising systems' performance by changing their structure, behaviour, or environment variables. Approaches from both areas rely on feedback loops that accumulate knowledge from the system interactions to inform autonomous decision-making. However, this knowledge is often limited, constraining the systems' interpretability and adaptability. This paper proposes a new concept for interpretable and adaptable software systems: self-sustaining software systems (S4). S4 builds knowledge loops between all available knowledge sources that define modern software systems to improve their interpretability and adaptability. This paper introduces and discusses the S4 concept.
}
}

Endnote

%0 Conference Paper
%T Self-sustaining software systems (S4): Towards improved interpretability and adaptation
%A Christian Cabrera
%A Andrei Paleyes
%A Neil D. Lawrence
%B Proceedings of the 1st International Workshop on New Trends in Software Engineering
%D 2024	
%F publications/self-sustaining-software-systems
%U /publications/self-sustaining-software-systems.html
%X Software systems impact society at different levels as they pervasively solve real-world problems. Modern software systems are often so sophisticated that their complexity exceeds the limits of human comprehension. These systems must respond to changing goals, dynamic data, unexpected failures, and security threats, among other variable factors in real-world environments. Systems' complexity challenges their interpretability and requires autonomous responses to dynamic changes. Two main research areas explore autonomous systems' responses: evolutionary computing and autonomic computing. Evolutionary computing focuses on software improvement based on iterative modifications to the source code. Autonomic computing focuses on optimising systems' performance by changing their structure, behaviour, or environment variables. Approaches from both areas rely on feedback loops that accumulate knowledge from the system interactions to inform autonomous decision-making. However, this knowledge is often limited, constraining the systems' interpretability and adaptability. This paper proposes a new concept for interpretable and adaptable software systems: self-sustaining software systems (S4). S4 builds knowledge loops between all available knowledge sources that define modern software systems to improve their interpretability and adaptability. This paper introduces and discusses the S4 concept.

RIS


TY  - CPAPER
TI  - Self-sustaining software systems (S4): Towards improved interpretability and adaptation
AU  - Christian Cabrera
AU  - Andrei Paleyes
AU  - Neil D. Lawrence
BT  - Proceedings of the 1st International Workshop on New Trends in Software Engineering
DA  - 2024/01/21	
ID  - publications/self-sustaining-software-systems
UR  - /publications/self-sustaining-software-systems.html
AB  - Software systems impact society at different levels as they pervasively solve real-world problems. Modern software systems are often so sophisticated that their complexity exceeds the limits of human comprehension. These systems must respond to changing goals, dynamic data, unexpected failures, and security threats, among other variable factors in real-world environments. Systems' complexity challenges their interpretability and requires autonomous responses to dynamic changes. Two main research areas explore autonomous systems' responses: evolutionary computing and autonomic computing. Evolutionary computing focuses on software improvement based on iterative modifications to the source code. Autonomic computing focuses on optimising systems' performance by changing their structure, behaviour, or environment variables. Approaches from both areas rely on feedback loops that accumulate knowledge from the system interactions to inform autonomous decision-making. However, this knowledge is often limited, constraining the systems' interpretability and adaptability. This paper proposes a new concept for interpretable and adaptable software systems: self-sustaining software systems (S4). S4 builds knowledge loops between all available knowledge sources that define modern software systems to improve their interpretability and adaptability. This paper introduces and discusses the S4 concept.

ER  -

APA


Cabrera, C., Paleyes, A. & Lawrence, N.D.. (2024). Self-sustaining software systems (S4): Towards improved interpretability and adaptation. Proceedings of the 1st International Workshop on New Trends in Software Engineering Available from /publications/self-sustaining-software-systems.html.

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