Adaptive Immunity for Software: Towards Autonomous Self-healing Systems

TL;DR

This paper explores the potential of using artificial immune systems to develop autonomous self-healing software, emphasizing machine learning for behavior modeling and anomaly detection to improve system robustness.

Contribution

It proposes a novel research agenda for integrating AISs into self-healing software, highlighting the advantages over traditional model-driven approaches.

Findings

Survey of current self-healing systems and AIS research

Identification of machine learning as a means to learn system behavior

Proposed research directions for AIS-based self-healing software

Abstract

Testing and code reviews are known techniques to improve the quality and robustness of software. Unfortunately, the complexity of modern software systems makes it impossible to anticipate all possible problems that can occur at runtime, which limits what issues can be found using testing and reviews. Thus, it is of interest to consider autonomous self-healing software systems, which can automatically detect, diagnose, and contain unanticipated problems at runtime. Most research in this area has adopted a model-driven approach, where actual behavior is checked against a model specifying the intended behavior, and a controller takes action when the system behaves outside of the specification. However, it is not easy to develop these specifications, nor to keep them up-to-date as the system evolves. We pose that, with the recent advances in machine learning, such models may be learned by observing the system. Moreover, we argue that artificial immune systems (AISs) are particularly well-suited for building self-healing systems, because of their anomaly detection and diagnosis capabilities. We present the state-of-the-art in self-healing systems and in AISs, surveying some of the research directions that have been considered up to now. To help advance the state-of-the-art, we develop a research agenda for building self-healing software systems using AISs, identifying required foundations, and promising research directions.