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

arXiv: https://arxiv.org/abs/2401.11370 · HTML: https://mlsanity.com/paper/2401.11370/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 21 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Understanding accountability in algorithmic supply chains](https://mlsanity.com/paper/2304.14749/) (2023)
- [Knowledge Graphs and Knowledge Networks: The Story in Brief](https://mlsanity.com/paper/2003.03623/) (2020)
- [NOUS: Construction and Querying of Dynamic Knowledge Graphs](https://mlsanity.com/paper/1606.02314/) (2016)
- [Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains](https://mlsanity.com/paper/2101.00125/) (2021)
- [Dataflow graphs as complete causal graphs](https://mlsanity.com/paper/2303.09552/) (2023)
- [Machine Learning Systems: A Survey from a Data-Oriented Perspective](https://mlsanity.com/paper/2302.04810/) (2023)

## Built on by (most cited first, 2 of 2)

- [The Systems Engineering Approach in Times of Large Language Models](https://mlsanity.com/paper/2411.09050/) (2024)
- [LLM Performance for Code Generation on Noisy Tasks](https://mlsanity.com/paper/2505.23598/) (2025)

## Abstract (arXiv)

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.