Evolve the Model Universe of a System Universe

TL;DR

This paper proposes a vision to enhance the trustworthiness of evolving intelligent software systems by combining software engineering, evolutionary computation, and machine learning to support continuous model universe evolution.

Contribution

It introduces a novel approach integrating multiple techniques to enable ongoing evolution and refinement of the model universe for trustworthy decision making.

Findings

Conceptual framework for model universe evolution

Potential integration of software engineering and machine learning techniques

Foundation for future implementation and validation

Abstract

Uncertain, unpredictable, real time, and lifelong evolution causes operational failures in intelligent software systems, leading to significant damages, safety and security hazards, and tragedies. To fully unleash the potential of such systems and facilitate their wider adoption, ensuring the trustworthiness of their decision making under uncertainty is the prime challenge. To overcome this challenge, an intelligent software system and its operating environment should be continuously monitored, tested, and refined during its lifetime operation. Existing technologies, such as digital twins, can enable continuous synchronisation with such systems to reflect their most updated states. Such representations are often in the form of prior knowledge based and machine learning models, together called model universe. In this paper, we present our vision of combining techniques from software engineering, evolutionary computation, and machine learning to support the model universe evolution.