Coupling in software applications is often used as an indicator of external quality attributes such as fault-proneness. In fact, the correlation of coupling metrics and faults in object oriented programs has been widely studied. However, there is very limited knowledge about which coupling properties in aspect-oriented programming (AOP) are effective indicators of faults in modules. Existing coupling metrics do not take into account the specificities of AOP mechanisms. As a result, these metrics are unlikely to provide optimal predictions of pivotal quality attributes such as fault-proneness. This impacts further by restraining the assessments of AOP empirical studies. To address these issues, this paper presents an empirical study to evaluate the impact of coupling sourced from AOP-specific mechanisms. We utilise a novel set of coupling metrics to predict fault occurrences in aspect-oriented programs. We also compare these new metrics against previously proposed metrics for AOP. More specifically, we analyse faults from several releases of three AspectJ applications and perform statistical analyses to reveal the effectiveness of these metrics when predicting faults. Our study shows that a particular set of fine-grained directed coupling metrics have the potential to help create better fault prediction models for AO programs.