We provide a new perspective on the problem how high-level state machine models with abstract actions can be related to low-level models in which these actions are refined by sequences of concrete actions. We describe the connection between high-level and low-level actions using \emph{action codes}, a variation of the prefix codes known from coding theory. For each action code ${\mathcal{R}}$, we introduce a \emph{contraction} operator $α_{\mathcal{R}}$ that turns a low-level model $\mathcal{M}$ into a high-level model, and a \emph{refinement} operator $ρ_{\mathcal{R}}$ that transforms a high-level model $\mathcal{N}$ into a low-level model. We establish a Galois connection $ρ_{\mathcal{R}}(\mathcal{N}) \sqsubseteq \mathcal{M} \Leftrightarrow \mathcal{N} \sqsubseteq α_{\mathcal{R}}(\mathcal{M})$, where $\sqsubseteq$ is the well-known simulation preorder. For conformance, we typically want to obtain an overapproximation of model $\mathcal{M}$. To this end, we also introduce a \emph{concretization} operator $γ_{\mathcal{R}}$, which behaves like the refinement operator but adds arbitrary behavior at intermediate points, giving us a second Galois connection $α_{\mathcal{R}}(\mathcal{M}) \sqsubseteq \mathcal{N} \Leftrightarrow \mathcal{M} \sqsubseteq γ_{\mathcal{R}}(\mathcal{N})$. Action codes may be used to construct adaptors that translate between concrete and abstract actions during learning and testing of Mealy machines. If Mealy machine $\mathcal{M}$ models a black-box system then $α_{\mathcal{R}}(\mathcal{M})$ describes the behavior that can be observed by a learner/tester that interacts with this system via an adaptor derived from code ${\mathcal{R}}$. Whenever $α_{\mathcal{R}}(\mathcal{M})$ implements (or conforms to) $\mathcal{N}$, we may conclude that $\mathcal{M}$ implements (or conforms to) $γ_{\mathcal{R}} (\mathcal{N})$.