diff --git a/src/Algorithms/CARLIN/reach.jl b/src/Algorithms/CARLIN/reach.jl
index 422fce111..194be13e9 100644
--- a/src/Algorithms/CARLIN/reach.jl
+++ b/src/Algorithms/CARLIN/reach.jl
@@ -57,7 +57,7 @@ end
 function _compute_resets(resets::Vector{Float64}, T)
     # assumes initial time is 0
     aux = vcat(0.0, resets, T)
-    return [interval(aux[i], aux[i + 1]) for i in 1:(length(aux) - 1)]
+    return [IA.interval(aux[i], aux[i + 1]) for i in 1:(length(aux) - 1)]
 end
 
 function reach_CARLIN_resets(X0, F1, F2, resets; alg, N, T, Δt, bloat, compress)
diff --git a/src/Discretization/Overapproximate.jl b/src/Discretization/Overapproximate.jl
index d2716b4ea..0bcb7b8b7 100644
--- a/src/Discretization/Overapproximate.jl
+++ b/src/Discretization/Overapproximate.jl
@@ -1,7 +1,6 @@
 module Overapproximate
 
 using LazySets
-import LazySets: _split
 using IntervalMatrices
 using StaticArrays: SVector, SMatrix, MMatrix, StaticArray
 import IntervalArithmetic as IA
diff --git a/src/ReachSets/AbstractReachSet.jl b/src/ReachSets/AbstractReachSet.jl
index c31732378..6793c5f7c 100644
--- a/src/ReachSets/AbstractReachSet.jl
+++ b/src/ReachSets/AbstractReachSet.jl
@@ -67,7 +67,7 @@ Return the type of the set representation of this reach-set.
 
 Type of the set representation of the given reach-set.
 """
-setrep(::AbstractReachSet)
+function setrep(::AbstractReachSet) end
 
 """
     tstart(R::AbstractReachSet)
diff --git a/src/ReachSets/TaylorModelReachSet.jl b/src/ReachSets/TaylorModelReachSet.jl
index 624b82899..06030a2ba 100644
--- a/src/ReachSets/TaylorModelReachSet.jl
+++ b/src/ReachSets/TaylorModelReachSet.jl
@@ -501,7 +501,7 @@ function _overapproximate_structured(Z::AbstractZonotope{N}, ::Type{<:TaylorMode
     @inbounds for i in 1:n
         pi = c[i] + sum(view(M, i, :) .* x)
         di = abs(D[i, i])
-        rem = interval(-di, di)
+        rem = IA.interval(-di, di)
         vTM[i] = TaylorModel1(Taylor1(pi, orderT), rem, zeroI, Δtn)
     end
 
@@ -543,7 +543,7 @@ function _overapproximate_structured(Zcp::CartesianProduct{N,<:Zonotope,<:Interv
     @inbounds begin
         pi = mid(Y.dat) + zero(TaylorN(1; order=orderQ))
         d = diam(Y.dat) / 2
-        rem = interval(-d, d)
+        rem = IA.interval(-d, d)
         vTM[n] = TaylorModel1(Taylor1(pi, orderT), rem, zeroI, Δtn)
     end
     return TaylorModelReachSet(vTM, Δt)
@@ -574,7 +574,7 @@ function _overapproximate_structured_full(Zcp::CartesianProduct{N,<:Zonotope,<:I
     @inbounds for i in 1:n
         pi = c[i] + sum(view(G, i, 1:(n + 1)) .* x) + zero(TaylorN(n + 1; order=orderQ))
         d = abs(G[i, n + 1 + i])
-        rem = interval(-d, d)
+        rem = IA.interval(-d, d)
         vTM[i] = TaylorModel1(Taylor1(pi, orderT), rem, zeroI, Δtn)
     end
 
@@ -582,7 +582,7 @@ function _overapproximate_structured_full(Zcp::CartesianProduct{N,<:Zonotope,<:I
     I = Zcp.Y.dat
     pi = mid(I) + zero(TaylorN(n + 1; order=orderQ))
     d = diam(I) / 2
-    rem = interval(-d, d)
+    rem = IA.interval(-d, d)
     @inbounds vTM[n + 1] = TaylorModel1(Taylor1(pi, orderT), rem, zeroI, Δtn)
 
     return TaylorModelReachSet(vTM, Δt)