diff --git a/src/pattern.jl b/src/pattern.jl
index d2df6659..1ea72434 100644
--- a/src/pattern.jl
+++ b/src/pattern.jl
@@ -16,6 +16,23 @@ function trace_input(::Type{T}, x::AbstractArray, i) where {T<:AbstractTracer}
     return tracer.(T, indices)
 end
 
+## Trace function
+function trace_function(::Type{T}, f, x) where {T<:AbstractTracer}
+    xt = trace_input(T, x)
+    yt = f(xt)
+    return xt, yt
+end
+
+function trace_function(::Type{T}, f!, y, x) where {T<:AbstractTracer}
+    xt = trace_input(T, x)
+    yt = similar(y, T)
+    f!(yt, xt)
+    return xt, yt
+end
+
+to_array(x::Number) = [x]
+to_array(x::AbstractArray) = x
+
 ## Construct sparsity pattern matrix
 """
     connectivity_pattern(f, x)
@@ -40,8 +57,10 @@ julia> connectivity_pattern(f, x)
  ⋅  ⋅  1
 ```
 """
-connectivity_pattern(f, x, settype::Type{S}=DEFAULT_SET_TYPE) where {S} =
-    pattern(f, ConnectivityTracer{S}, x)
+function connectivity_pattern(f, x, ::Type{S}=DEFAULT_SET_TYPE) where {S}
+    xt, yt = trace_function(ConnectivityTracer{S}, f, x)
+    return connectivity_pattern_to_mat(to_array(xt), to_array(yt))
+end
 
 """
     connectivity_pattern(f!, y, x)
@@ -53,7 +72,27 @@ where `C[i, j]` is true if the compute graph connects the `i`-th entry in `y` to
 The type of index set `S` can be specified as an optional argument and defaults to `BitSet`.
 """
 function connectivity_pattern(f!, y, x, ::Type{S}=DEFAULT_SET_TYPE) where {S}
-    return pattern(f!, y, ConnectivityTracer{S}, x)
+    xt, yt = trace_function(ConnectivityTracer{S}, f!, y, x)
+    return connectivity_pattern_to_mat(to_array(xt), to_array(yt))
+end
+
+function connectivity_pattern_to_mat(
+    xt::AbstractArray{T}, yt::AbstractArray{<:Number}
+) where {T<:ConnectivityTracer}
+    n, m = length(xt), length(yt)
+    I = UInt64[] # row indices
+    J = UInt64[] # column indices
+    V = Bool[]   # values
+    for (i, y) in enumerate(yt)
+        if y isa T
+            for j in inputs(y)
+                push!(I, i)
+                push!(J, j)
+                push!(V, true)
+            end
+        end
+    end
+    return sparse(I, J, V, m, n)
 end
 
 """
@@ -79,7 +118,8 @@ julia> jacobian_pattern(f, x)
 ```
 """
 function jacobian_pattern(f, x, ::Type{S}=DEFAULT_SET_TYPE) where {S}
-    return pattern(f, JacobianTracer{S}, x)
+    xt, yt = trace_function(JacobianTracer{S}, f, x)
+    return jacobian_pattern_to_mat(to_array(xt), to_array(yt))
 end
 
 """
@@ -91,7 +131,27 @@ Compute the sparsity pattern of the Jacobian of `f!(y, x)`.
 The type of index set `S` can be specified as an optional argument and defaults to `BitSet`.
 """
 function jacobian_pattern(f!, y, x, ::Type{S}=DEFAULT_SET_TYPE) where {S}
-    return pattern(f!, y, JacobianTracer{S}, x)
+    xt, yt = trace_function(JacobianTracer{S}, f!, y, x)
+    return jacobian_pattern_to_mat(to_array(xt), to_array(yt))
+end
+
+function jacobian_pattern_to_mat(
+    xt::AbstractArray{T}, yt::AbstractArray{<:Number}
+) where {T<:JacobianTracer}
+    n, m = length(xt), length(yt)
+    I = UInt64[] # row indices
+    J = UInt64[] # column indices
+    V = Bool[]   # values
+    for (i, y) in enumerate(yt)
+        if y isa T
+            for j in inputs(y)
+                push!(I, i)
+                push!(J, j)
+                push!(V, true)
+            end
+        end
+    end
+    return sparse(I, J, V, m, n)
 end
 
 """
@@ -129,63 +189,21 @@ julia> hessian_pattern(g, x)
 ```
 """
 function hessian_pattern(f, x, ::Type{S}=DEFAULT_SET_TYPE) where {S}
-    return pattern(f, HessianTracer{S}, x)
-end
-
-function pattern(f, ::Type{T}, x) where {T<:AbstractTracer}
-    xt = trace_input(T, x)
-    yt = f(xt)
-    return _pattern(xt, yt)
-end
-
-function pattern(f!, y, ::Type{T}, x) where {T<:AbstractTracer}
-    xt = trace_input(T, x)
-    yt = similar(y, T)
-    f!(yt, xt)
-    return _pattern(xt, yt)
-end
-
-_pattern(xt::AbstractTracer, yt::Number) = _pattern([xt], [yt])
-_pattern(xt::AbstractTracer, yt::AbstractArray{<:Number}) = _pattern([xt], yt)
-_pattern(xt::AbstractArray{<:AbstractTracer}, yt::Number) = _pattern(xt, [yt])
-function _pattern(xt::AbstractArray{<:AbstractTracer}, yt::AbstractArray{<:Number})
-    return _pattern_to_sparsemat(xt, yt)
+    xt, yt = trace_function(HessianTracer{S}, f, x)
+    return hessian_pattern_to_mat(to_array(xt), yt)
 end
 
-function _pattern_to_sparsemat(
-    xt::AbstractArray{T}, yt::AbstractArray{<:Number}
-) where {T<:AbstractTracer}
-    # Construct matrix of size (ouput_dim, input_dim)
-    n, m = length(xt), length(yt)
-    I = UInt64[] # row indices
-    J = UInt64[] # column indices
-    V = Bool[]   # values
-    for (i, y) in enumerate(yt)
-        if y isa T
-            for j in inputs(y)
-                push!(I, i)
-                push!(J, j)
-                push!(V, true)
-            end
-        end
-    end
-    return sparse(I, J, V, m, n)
-end
-
-function _pattern_to_sparsemat(
-    xt::AbstractArray{HessianTracer{S}}, yt::AbstractArray{HessianTracer{S}}
+function hessian_pattern_to_mat(
+    xt::AbstractArray{HessianTracer{S}}, yt::HessianTracer{S}
 ) where {S}
-    length(yt) != 1 && error("pattern(f, HessianTracer, x) expects scalar output y=f(x).")
-    y = only(yt)
-
     # Allocate Hessian matrix
     n = length(xt)
     I = UInt64[] # row indices
     J = UInt64[] # column indices
     V = Bool[]   # values
 
-    for i in keys(y.inputs)
-        for j in y.inputs[i]
+    for i in keys(yt.inputs)
+        for j in yt.inputs[i]
             push!(I, i)
             push!(J, j)
             push!(V, true)