How do we Evaluate Self-adaptive Software Systems?
This addresses the problem for researchers in software engineering by providing insights to improve experimental comparisons, though it is incremental as it synthesizes existing practices rather than introducing new methods.
The paper tackled the lack of clear evaluation methods in self-adaptive software systems by conducting a mapping study of experimental evaluations from the SEAMS symposium over the last decade, identifying key challenges such as distinguishing managing vs. managed systems and handling uncertainties.
With the increase of research in self-adaptive systems, there is a need to better understand the way research contributions are evaluated. Such insights will support researchers to better compare new findings when developing new knowledge for the community. However, so far there is no clear overview of how evaluations are performed in self-adaptive systems. To address this gap, we conduct a mapping study. The study focuses on experimental evaluations published in the last decade at the prime venue of research in software engineering for self-adaptive systems -- the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Results point out that specifics of self-adaptive systems require special attention in the experimental process, including the distinction of the managing system (i.e., the target of evaluation) and the managed system, the presence of uncertainties that affect the system behavior and hence need to be taken into account in data analysis, and the potential of managed systems to be reused across experiments, beyond replications. To conclude, we offer a set of suggestions derived from our study that can be used as input to enhance future experiments in self-adaptive systems.