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Software systems impact society at different levels as they pervasively solvereal-world problems. Modern software systems are often so sophisticated thattheir complexity exceeds the limits of human comprehension. These systems mustrespond to changing goals, dynamic data, unexpected failures, and securitythreats, among other variable factors in real-world environments. Systems'complexity challenges their interpretability and requires autonomous responsesto dynamic changes. Two main research areas explore autonomous systems'responses: evolutionary computing and autonomic computing. Evolutionarycomputing focuses on software improvement based on iterative modifications tothe source code. Autonomic computing focuses on optimising systems' performanceby changing their structure, behaviour, or environment variables. Approachesfrom both areas rely on feedback loops that accumulate knowledge from thesystem interactions to inform autonomous decision-making. However, thisknowledge is often limited, constraining the systems' interpretability andadaptability. This paper proposes a new concept for interpretable and adaptablesoftware systems: self-sustaining software systems (S4). S4 builds knowledgeloops between all available knowledge sources that define modern softwaresystems to improve their interpretability and adaptability. This paperintroduces and discusses the S4 concept.

Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation