Monte-Carlo simulations play a key role in the current AOCS V&V process, but it is generally time-consuming and it may fail in detecting worst-case configurations, especially in the presence of rare events. In such a case, µ-analysis offers a nice alternative, although it cannot measure the probability of occurrence of the identified worst-cases, which can invalidate a control system on the basis of unlikely events. Probabilistic µ-analysis was introduced in this context 20 years ago to bridge the gap between the two techniques, but until recently no practical tools were available. This paper summarizes recent advances on this topic with a particular emphasis on practical applications to space systems. More precisely, the proposed technique is applied to evaluate AOCS controllers in the context of a challenging high accuracy satellite pointing control problem. The way the proposed tools can be integrated into the traditional AOCS V&V process and used to tighten the V&V analysis gap is also highlighted.