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Genetic configuration sampling: learning a sampling strategy for fault detection of configurable systems

A highly-configurable system provides many configuration options to diversify application scenarios. The combination of these configuration options results in a large search space of configurations. This makes the detection of configuration-related faults extremely hard. Since it is infeasible to exhaust every configuration, several methods are proposed to sample a subset of all configurations to detect hidden faults. Configuration sampling can be viewed as a process of repeating a pre-defined sampling action to the whole search space, such as the one-enabled or pair-wise strategy. In this paper, we propose genetic configuration sampling, a new method of learning a sampling strategy for configuration-related faults. Genetic configuration sampling encodes a sequence of sampling actions as a chromosome in the genetic algorithm. Given a set of known configuration-related faults, genetic configuration sampling evolves the sequence of sampling actions and applies the learnt sequence to new configuration data. A pilot study on three highly-configurable systems shows that genetic configuration sampling performs well among nine sampling strategies in comparison.