diff --git a/Project.toml b/Project.toml
index 1c3c5541f..c166a2053 100644
--- a/Project.toml
+++ b/Project.toml
@@ -50,7 +50,7 @@ Random = "1.6"
 RandomNumbers = "1.5.3"
 RecursiveArrayTools = "2, 3"
 Reexport = "0.2, 1.0"
-SciMLBase = "2.0.6"
+SciMLBase = "2.51"
 SciMLOperators = "0.2.9, 0.3"
 SparseArrays = "1.6"
 SparseDiffTools = "2"
diff --git a/src/alg_utils.jl b/src/alg_utils.jl
index 589f938cb..b7b152565 100644
--- a/src/alg_utils.jl
+++ b/src/alg_utils.jl
@@ -133,22 +133,22 @@ beta1_default(alg::Union{StochasticDiffEqAlgorithm,StochasticDiffEqRODEAlgorithm
 
 isdtchangeable(alg::Union{StochasticDiffEqAlgorithm,StochasticDiffEqRODEAlgorithm}) = true
 
-SciMLBase.alg_interpretation(alg::StochasticDiffEqAlgorithm) = :Ito
-SciMLBase.alg_interpretation(alg::EulerHeun) = :Stratonovich
-SciMLBase.alg_interpretation(alg::LambaEulerHeun) = :Stratonovich
-SciMLBase.alg_interpretation(alg::KomBurSROCK2) = :Stratonovich
+SciMLBase.alg_interpretation(alg::StochasticDiffEqAlgorithm) = SciMLBase.AlgorithmInterpretation.Ito
+SciMLBase.alg_interpretation(alg::EulerHeun) = SciMLBase.AlgorithmInterpretation.Stratonovich
+SciMLBase.alg_interpretation(alg::LambaEulerHeun) = SciMLBase.AlgorithmInterpretation.Stratonovich
+SciMLBase.alg_interpretation(alg::KomBurSROCK2) = SciMLBase.AlgorithmInterpretation.Stratonovich
 SciMLBase.alg_interpretation(alg::RKMil{interpretation}) where {interpretation} = interpretation
 SciMLBase.alg_interpretation(alg::SROCK1{interpretation,E}) where {interpretation,E} = interpretation
 SciMLBase.alg_interpretation(alg::RKMilCommute) = alg.interpretation
 SciMLBase.alg_interpretation(alg::RKMilGeneral) = alg.interpretation
 SciMLBase.alg_interpretation(alg::ImplicitRKMil{CS,AD,F,P,FDT,ST,CJ,N,T2,Controller,interpretation}) where {CS,AD,F,P,FDT,ST,CJ,N,T2,Controller,interpretation} = interpretation
 
-SciMLBase.alg_interpretation(alg::RS1) = :Stratonovich
-SciMLBase.alg_interpretation(alg::RS2) = :Stratonovich
+SciMLBase.alg_interpretation(alg::RS1) = SciMLBase.AlgorithmInterpretation.Stratonovich
+SciMLBase.alg_interpretation(alg::RS2) = SciMLBase.AlgorithmInterpretation.Stratonovich
 
-SciMLBase.alg_interpretation(alg::NON) = :Stratonovich
-SciMLBase.alg_interpretation(alg::COM) = :Stratonovich
-SciMLBase.alg_interpretation(alg::NON2) = :Stratonovich
+SciMLBase.alg_interpretation(alg::NON) = SciMLBase.AlgorithmInterpretation.Stratonovich
+SciMLBase.alg_interpretation(alg::COM) = SciMLBase.AlgorithmInterpretation.Stratonovich
+SciMLBase.alg_interpretation(alg::NON2) = SciMLBase.AlgorithmInterpretation.Stratonovich
 
 alg_compatible(prob, alg::Union{StochasticDiffEqAlgorithm,StochasticDiffEqRODEAlgorithm}) = true
 alg_compatible(prob, alg::StochasticDiffEqAlgorithm) = false
diff --git a/src/algorithms.jl b/src/algorithms.jl
index b38800595..c6f124904 100644
--- a/src/algorithms.jl
+++ b/src/algorithms.jl
@@ -77,11 +77,11 @@ Springer. Berlin Heidelberg (2011)
 
 RKMil: Nonstiff Method
 An explicit Runge-Kutta discretization of the strong order 1.0 Milstein method.
-Defaults to solving the Ito problem, but RKMil(interpretation=:Stratonovich) makes it solve the Stratonovich problem.
+Defaults to solving the Ito problem, but RKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich) makes it solve the Stratonovich problem.
 Only handles scalar and diagonal noise.
 """
 struct RKMil{interpretation} <: StochasticDiffEqAdaptiveAlgorithm end
-RKMil(;interpretation=:Ito) = RKMil{interpretation}()
+RKMil(;interpretation=SciMLBase.AlgorithmInterpretation.Ito) = RKMil{interpretation}()
 
 """
 Kloeden, P.E., Platen, E., Numerical Solution of Stochastic Differential Equations.
@@ -89,37 +89,37 @@ Springer. Berlin Heidelberg (2011)
 
 RKMilCommute: Nonstiff Method
 An explicit Runge-Kutta discretization of the strong order 1.0 Milstein method for commutative noise problems.
-Defaults to solving the Ito problem, but RKMilCommute(interpretation=:Stratonovich) makes it solve the Stratonovich problem.
+Defaults to solving the Ito problem, but RKMilCommute(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich) makes it solve the Stratonovich problem.
 Uses a 1.5/2.0 error estimate for adaptive time stepping.
 Default: ii_approx=IICommutative() does not approximate the Levy area.
 """
 struct RKMilCommute{T} <: StochasticDiffEqAdaptiveAlgorithm 
-  interpretation::Symbol
+  interpretation::SciMLBase.AlgorithmInterpretation.T
   ii_approx::T
 end
-RKMilCommute(;interpretation=:Ito, ii_approx=IICommutative()) = RKMilCommute(interpretation,ii_approx)
+RKMilCommute(;interpretation=SciMLBase.AlgorithmInterpretation.Ito, ii_approx=IICommutative()) = RKMilCommute(interpretation,ii_approx)
 
 """
 Kloeden, P.E., Platen, E., Numerical Solution of Stochastic Differential Equations.
 Springer. Berlin Heidelberg (2011)
 
 RKMilGeneral: Nonstiff Method
-RKMilGeneral(;interpretation=:Ito, ii_approx=IILevyArea()
+RKMilGeneral(;interpretation=SciMLBase.AlgorithmInterpretation.Ito, ii_approx=IILevyArea()
 An explicit Runge-Kutta discretization of the strong order 1.0 Milstein method for general non-commutative noise problems.
-Allows for a choice of interpretation between :Ito and :Stratonovich.
+Allows for a choice of interpretation between SciMLBase.AlgorithmInterpretation.Ito and SciMLBase.AlgorithmInterpretation.Stratonovich.
 Allows for a choice of iterated integral approximation.
 Default: ii_approx=IILevyArea() uses LevyArea.jl to choose optimal algorithm. See
 Kastner, F. and Rößler, A., arXiv: 2201.08424
 Kastner, F. and Rößler, A., LevyArea.jl, 10.5281/ZENODO.5883748, https://github.com/stochastics-uni-luebeck/LevyArea.jl
 """
 struct RKMilGeneral{T, TruncationType} <: StochasticDiffEqAdaptiveAlgorithm
-  interpretation::Symbol
+  interpretation::SciMLBase.AlgorithmInterpretation.T
   ii_approx::T
   c::Int
   p::TruncationType
 end
 
-function RKMilGeneral(;interpretation=:Ito,ii_approx=IILevyArea(), c=1, p=nothing, dt=nothing)
+function RKMilGeneral(;interpretation=SciMLBase.AlgorithmInterpretation.Ito,ii_approx=IILevyArea(), c=1, p=nothing, dt=nothing)
   γ = 1//1
   p==true && (p = Int(floor(c*dt^(1//1-2//1*γ)) + 1))
   RKMilGeneral{typeof(ii_approx), typeof(p)}(interpretation, ii_approx, c, p)
@@ -160,13 +160,13 @@ struct WangLi3SMil_F <: StochasticDiffEqAlgorithm end
 """
 SROCK1: S-ROCK Method
 Is a fixed step size stabilized explicit method for stiff problems.
-Defaults to solving th Ito problem but SROCK1(interpretation=:Stratonovich) can make it solve the Stratonovich problem.
+Defaults to solving th Ito problem but SROCK1(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich) can make it solve the Stratonovich problem.
 Strong order of convergence is 0.5 and weak order 1, but is optimised to get order 1 in case os scalar/diagonal noise.
 """
 struct SROCK1{interpretation,E} <: StochasticDiffEqAlgorithm
   eigen_est::E
 end
-SROCK1(;interpretation=:Ito,eigen_est=nothing) = SROCK1{interpretation,typeof(eigen_est)}(eigen_est)
+SROCK1(;interpretation=SciMLBase.AlgorithmInterpretation.Ito,eigen_est=nothing) = SROCK1{interpretation,typeof(eigen_est)}(eigen_est)
 
 # Weak Order 2
 for Alg in [:SROCK2, :KomBurSROCK2, :SROCKC2]
@@ -701,7 +701,7 @@ ImplicitEulerHeun(;chunk_size=0,autodiff=true,diff_type=Val{:central},
 ImplicitRKMil: Stiff Method
 An order 1.0 drift-implicit method.
 This is a theta method which defaults to theta=1 or the Trapezoid method on the drift term.
-Defaults to solving the Ito problem, but ImplicitRKMil(interpretation=:Stratonovich) makes it solve the Stratonovich problem.
+Defaults to solving the Ito problem, but ImplicitRKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich) makes it solve the Stratonovich problem.
 This method defaults to symplectic=false, but when true and theta=1/2 this is the implicit Midpoint method on the drift term and is symplectic in distribution.
 Handles diagonal and scalar noise. Uses a 1.5/2.0 heuristic for adaptive time stepping.
 """
@@ -721,7 +721,7 @@ ImplicitRKMil(;chunk_size=0,autodiff=true,diff_type=Val{:central},
                           extrapolant=:constant,
                           theta = 1,symplectic = false,
                           new_jac_conv_bound = 1e-3,
-                          controller = :Predictive,interpretation=:Ito) =
+                          controller = :Predictive,interpretation=SciMLBase.AlgorithmInterpretation.Ito) =
                           ImplicitRKMil{chunk_size,autodiff,
                           typeof(linsolve),typeof(precs),diff_type,
                           OrdinaryDiffEq._unwrap_val(standardtag),
diff --git a/src/perform_step/SROCK_perform_step.jl b/src/perform_step/SROCK_perform_step.jl
index 5e9eba744..9825d8d53 100644
--- a/src/perform_step/SROCK_perform_step.jl
+++ b/src/perform_step/SROCK_perform_step.jl
@@ -14,7 +14,7 @@
   cosh_inv = log(ω₀ + Sqrt_ω)             # arcosh(ω₀)
   ω₁ = (Sqrt_ω*cosh(mdeg*cosh_inv))/(mdeg*sinh(mdeg*cosh_inv))
 
-  if SciMLBase.alg_interpretation(integrator.alg) == :Stratonovich
+  if SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Stratonovich
     α  = cosh(mdeg*cosh_inv)/(2*ω₀*cosh((mdeg-1)*cosh_inv))
     γ  = 1/(2*α)
     β  = -γ
@@ -42,7 +42,7 @@
     k = integrator.f(uᵢ₋₁,p,tᵢ₋₁)
 
     u = dt*μ*k + ν*uᵢ₋₁ + κ*uᵢ₋₂
-    if (i > mdeg - 2) && SciMLBase.alg_interpretation(integrator.alg) == :Stratonovich
+    if (i > mdeg - 2) && SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Stratonovich
       if i == mdeg - 1
         gₘ₋₂ = integrator.g(uᵢ₋₁,p,tᵢ₋₁)
         if W.dW isa Number || !is_diagonal_noise(integrator.sol.prob)
@@ -58,7 +58,7 @@
           u .+= (β .* gₘ₋₂ .+ γ .* gₘ₋₁) .* W.dW
         end
       end
-    elseif (i == mdeg) && SciMLBase.alg_interpretation(integrator.alg) == :Ito
+    elseif (i == mdeg) && SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito
       if W.dW isa Number
         gₘ₋₂ = integrator.g(uᵢ₋₁,p,tᵢ₋₁)
         uᵢ₋₂ = uᵢ₋₁ + sqrt(abs(dt))*gₘ₋₂
@@ -105,7 +105,7 @@ end
   cosh_inv = log(ω₀ + Sqrt_ω)             # arcosh(ω₀)
   ω₁ = (Sqrt_ω*cosh(mdeg*cosh_inv))/(mdeg*sinh(mdeg*cosh_inv))
 
-  if SciMLBase.alg_interpretation(integrator.alg) == :Stratonovich
+  if SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Stratonovich
     α  = cosh(mdeg*cosh_inv)/(2*ω₀*cosh((mdeg-1)*cosh_inv))
     γ  = 1/(2*α)
     β  = -γ
@@ -132,7 +132,7 @@ end
     κ = - Tᵢ₋₂/Tᵢ
     integrator.f(k,uᵢ₋₁,p,tᵢ₋₁)
     @.. u = dt*μ*k + ν*uᵢ₋₁ + κ*uᵢ₋₂
-    if (i > mdeg - 2) && SciMLBase.alg_interpretation(integrator.alg) == :Stratonovich
+    if (i > mdeg - 2) && SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Stratonovich
       if i == mdeg - 1
         integrator.g(gₘ₋₂,uᵢ₋₁,p,tᵢ₋₁)
         if W.dW isa Number || is_diagonal_noise(integrator.sol.prob)
@@ -152,7 +152,7 @@ end
           @.. u += γ*k
         end
       end
-    elseif (i == mdeg) && SciMLBase.alg_interpretation(integrator.alg) == :Ito
+    elseif (i == mdeg) && SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito
       if W.dW isa Number || is_diagonal_noise(integrator.sol.prob)
         integrator.g(gₘ₋₂,uᵢ₋₁,p,tᵢ₋₁)
         @.. uᵢ₋₂ = uᵢ₋₁ + sqrt(abs(dt))*gₘ₋₂
diff --git a/src/perform_step/low_order.jl b/src/perform_step/low_order.jl
index 054ee5f54..72b06620c 100644
--- a/src/perform_step/low_order.jl
+++ b/src/perform_step/low_order.jl
@@ -208,16 +208,16 @@ end
   K = @.. uprev + dt * du1
   L = integrator.g(uprev,p,t)
   mil_correction = zero(u)
-  if SciMLBase.alg_interpretation(integrator.alg) == :Ito
+  if SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito
     utilde =  K + L*integrator.sqdt
     ggprime = (integrator.g(utilde,p,t).-L)./(integrator.sqdt)
     mil_correction = ggprime.*(W.dW.^2 .- abs(dt))./2
-  elseif SciMLBase.alg_interpretation(integrator.alg) == :Stratonovich
+  elseif SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Stratonovich
     utilde = uprev + L*integrator.sqdt
     ggprime = (integrator.g(utilde,p,t).-L)./(integrator.sqdt)
     mil_correction = ggprime.*(W.dW.^2)./2
   else
-    error("Alg interpretation invalid. Use either :Ito or :Stratonovich")
+    error("Algorithm interpretation invalid. Use either SciMLBase.AlgorithmInterpretation.Ito or SciMLBase.AlgorithmInterpretation.Stratonovich")
   end
   u = K+L.*W.dW+mil_correction
 
@@ -241,16 +241,16 @@ end
   integrator.g(L,uprev,p,t)
   @.. K = uprev + dt * du1
   @.. du2 = zero(eltype(u)) # This makes it safe to re-use the array
-  if SciMLBase.alg_interpretation(integrator.alg) == :Ito
+  if SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito
     @.. tmp = K + integrator.sqdt * L
     integrator.g(du2,tmp,p,t)
     @.. tmp = (du2-L)/(2integrator.sqdt)*(W.dW.^2 - abs(dt))
-  elseif SciMLBase.alg_interpretation(integrator.alg) == :Stratonovich
+  elseif SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Stratonovich
     @.. tmp = uprev + integrator.sqdt * L
     integrator.g(du2,tmp,p,t)
     @.. tmp = (du2-L)/(2integrator.sqdt)*(W.dW.^2)
   else
-    error("Alg interpretation invalid. Use either :Ito or :Stratonovich")
+    error("Algorithm interpretation invalid. Use either SciMLBase.AlgorithmInterpretation.Ito or SciMLBase.AlgorithmInterpretation.Stratonovich")
   end
   @.. u = K+L*W.dW + tmp
   if integrator.opts.adaptive
@@ -275,7 +275,7 @@ end
   J = get_iterated_I(dt, dW, W.dZ, Jalg)
 
   mil_correction = zero(u)
-  if SciMLBase.alg_interpretation(integrator.alg) == :Ito
+  if SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito
     if dW isa Number || is_diagonal_noise(integrator.sol.prob)
       J = J .- 1//2 .* abs(dt)
     else
@@ -289,7 +289,7 @@ end
   K = uprev + dt*du1
 
   if is_diagonal_noise(integrator.sol.prob)
-    tmp = (SciMLBase.alg_interpretation(integrator.alg) == :Ito ? K : uprev) .+ integrator.sqdt .* L
+    tmp = (SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito ? K : uprev) .+ integrator.sqdt .* L
     gtmp = integrator.g(tmp,p,t)
     Dgj = (gtmp - L)/sqdt
     ggprime_norm = integrator.opts.internalnorm(Dgj,t)
@@ -343,7 +343,7 @@ end
   J = Jalg.J
 
   @.. mil_correction = zero(u)
-  if SciMLBase.alg_interpretation(integrator.alg) == :Ito
+  if SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito
     if dW isa Number || is_diagonal_noise(integrator.sol.prob)
       @.. J -= 1 // 2 * abs(dt)
     else
@@ -357,7 +357,7 @@ end
   @.. K = uprev + dt*du1
 
   if is_diagonal_noise(integrator.sol.prob)
-    tmp .= (SciMLBase.alg_interpretation(integrator.alg) == :Ito ? K : uprev) .+ integrator.sqdt .* L
+    tmp .= (SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito ? K : uprev) .+ integrator.sqdt .* L
     integrator.g(gtmp,tmp,p,t)
     @.. Dgj = (gtmp - L)/sqdt
     ggprime_norm = integrator.opts.internalnorm(Dgj,t)
@@ -397,7 +397,7 @@ end
 
   J = get_iterated_I(dt, dW, W.dZ, Jalg, integrator.alg.p, integrator.alg.c, alg_order(integrator.alg))
 
-  if SciMLBase.alg_interpretation(integrator.alg) == :Ito
+  if SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito
     if dW isa Number || is_diagonal_noise(integrator.sol.prob)
       J = J .- 1//2 .* abs(dt)
     else
@@ -413,7 +413,7 @@ end
 
   if dW isa Number || is_diagonal_noise(integrator.sol.prob)
     K = @.. uprev + dt*du₁
-    utilde = (SciMLBase.alg_interpretation(integrator.alg) == :Ito ? K : uprev) + L*integrator.sqdt
+    utilde = (SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito ? K : uprev) + L*integrator.sqdt
     ggprime = (integrator.g(utilde,p,t) .- L) ./ (integrator.sqdt)
     mil_correction = ggprime .* J
     u = K + L .* dW + mil_correction
@@ -467,7 +467,7 @@ end
   @.. mil_correction = zero(eltype(u))
   ggprime_norm = zero(eltype(ggprime))
 
-  if SciMLBase.alg_interpretation(integrator.alg) == :Ito
+  if SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito
     if dW isa Number || is_diagonal_noise(integrator.sol.prob)
       @.. J -= 1 // 2 * abs(dt)
     else
@@ -478,7 +478,7 @@ end
   if dW isa Number || is_diagonal_noise(integrator.sol.prob)
     @.. K = uprev + dt*du₁
     @.. du₂ = zero(eltype(u))
-    tmp .= (SciMLBase.alg_interpretation(integrator.alg) == :Ito ? K : uprev) .+ integrator.sqdt .* L
+    tmp .= (SciMLBase.alg_interpretation(integrator.alg) == SciMLBase.AlgorithmInterpretation.Ito ? K : uprev) .+ integrator.sqdt .* L
     integrator.g(du₂,tmp,p,t)
     @.. ggprime = (du₂ - L)/sqdt
     ggprime_norm = integrator.opts.internalnorm(ggprime,t)
diff --git a/src/perform_step/sdirk.jl b/src/perform_step/sdirk.jl
index 7a3255688..d475a0096 100644
--- a/src/perform_step/sdirk.jl
+++ b/src/perform_step/sdirk.jl
@@ -24,21 +24,21 @@
   end
 
   if cache isa ImplicitRKMilConstantCache || integrator.opts.adaptive == true
-    if SciMLBase.alg_interpretation(alg) == :Ito ||
+    if SciMLBase.alg_interpretation(alg) == SciMLBase.AlgorithmInterpretation.Ito ||
        cache isa ImplicitEMConstantCache
       K = @.. uprev + dt * ftmp
       utilde =  K + L*integrator.sqdt
       ggprime = (integrator.g(utilde,p,t).-L)./(integrator.sqdt)
       mil_correction = ggprime .* (integrator.W.dW.^2 .- abs(dt))./2
       gtmp += mil_correction
-    elseif SciMLBase.alg_interpretation(alg) == :Stratonovich ||
+    elseif SciMLBase.alg_interpretation(alg) == SciMLBase.AlgorithmInterpretation.Stratonovich ||
            cache isa ImplicitEulerHeunConstantCache
       utilde = uprev + L*integrator.sqdt
       ggprime = (integrator.g(utilde,p,t).-L)./(integrator.sqdt)
       mil_correction = ggprime.*(integrator.W.dW.^2)./2
       gtmp += mil_correction
     else
-      error("Alg interpretation invalid. Use either :Ito or :Stratonovich")
+      error("Algorithm interpretation invalid. Use either SciMLBase.AlgorithmInterpretation.Ito or SciMLBase.AlgorithmInterpretation.Stratonovich")
     end
   end
 
@@ -154,18 +154,18 @@ end
 
   if cache isa ImplicitRKMilCache
     gtmp3 = cache.gtmp3
-    if SciMLBase.alg_interpretation(alg) == :Ito
+    if SciMLBase.alg_interpretation(alg) == SciMLBase.AlgorithmInterpretation.Ito
       @.. z = uprev + dt * tmp + integrator.sqdt * gtmp
       integrator.g(gtmp3,z,p,t)
       @.. gtmp3 = (gtmp3-gtmp)/(integrator.sqdt) # ggprime approximation
       @.. gtmp2 += gtmp3*(dW.^2 - abs(dt))/2
-    elseif SciMLBase.alg_interpretation(alg) == :Stratonovich
+    elseif SciMLBase.alg_interpretation(alg) == SciMLBase.AlgorithmInterpretation.Stratonovich
       @.. z = uprev + integrator.sqdt * gtmp
       integrator.g(gtmp3,z,p,t)
       @.. gtmp3 = (gtmp3-gtmp)/(integrator.sqdt) # ggprime approximation
       @.. gtmp2 += gtmp3*(dW.^2)/2
     else
-      error("Alg interpretation invalid. Use either :Ito or :Stratonovich")
+      error("Algorithm interpretation invalid. Use either SciMLBase.AlgorithmInterpretation.Ito or SciMLBase.AlgorithmInterpretation.Stratonovich")
     end
   end
 
diff --git a/test/adaptive/sde_complex_adaptive_mean_test.jl b/test/adaptive/sde_complex_adaptive_mean_test.jl
index 542fb6699..a427961c8 100644
--- a/test/adaptive/sde_complex_adaptive_mean_test.jl
+++ b/test/adaptive/sde_complex_adaptive_mean_test.jl
@@ -1,4 +1,5 @@
 using StochasticDiffEq, DiffEqCallbacks, DiffEqNoiseProcess, Test, Random
+import SciMLBase
 
 Random.seed!(100)
 # Definitions according to vector/matrix representations of operators from QuantumOptics
@@ -44,20 +45,20 @@ let
     out1 = SavedValues(Float64,ComplexF64)
     scb1 = SavingCallback(fout, out1, saveat=T, save_everystep=false, save_start=false)
 
-    solve(prob1, RKMil{:Stratonovich}(), dt=1e-4, callback=scb1, seed = i, adaptive = false)
+    solve(prob1, RKMil{SciMLBase.AlgorithmInterpretation.Stratonovich}(), dt=1e-4, callback=scb1, seed = i, adaptive = false)
     avg1 .+= out1.saveval ./ Ntraj
 
     out1 = SavedValues(Float64,ComplexF64)
     scb1 = SavingCallback(fout, out1, saveat=T, save_everystep=false, save_start=false)
 
-    solve(prob1, RKMil{:Stratonovich}(), tstops = T, callback=scb1,
+    solve(prob1, RKMil{SciMLBase.AlgorithmInterpretation.Stratonovich}(), tstops = T, callback=scb1,
           save_everystep=false, save_start=false)
     avg2 .+= out1.saveval ./ Ntraj
 
     out1 = SavedValues(Float64,ComplexF64)
     scb1 = SavingCallback(fout, out1, saveat=T, save_everystep=false, save_start=false)
 
-    solve(prob2, RKMil{:Stratonovich}(), tstops = T, callback=scb1,
+    solve(prob2, RKMil{SciMLBase.AlgorithmInterpretation.Stratonovich}(), tstops = T, callback=scb1,
           save_everystep=false, save_start=false)
     avg3 .+= out1.saveval ./ Ntraj
   end
@@ -88,20 +89,20 @@ let
     out1 = SavedValues(Float64,ComplexF64)
     scb1 = SavingCallback(fout, out1, saveat=T, save_everystep=false, save_start=false)
 
-    solve(prob1, RKMilGeneral(interpretation = :Stratonovich), dt=1e-4, callback=scb1, seed = i, adaptive = false)
+    solve(prob1, RKMilGeneral(interpretation = SciMLBase.AlgorithmInterpretation.Stratonovich), dt=1e-4, callback=scb1, seed = i, adaptive = false)
     avg1 .+= out1.saveval ./ Ntraj
 
     out1 = SavedValues(Float64,ComplexF64)
     scb1 = SavingCallback(fout, out1, saveat=T, save_everystep=false, save_start=false)
 
-    solve(prob1, RKMilGeneral(interpretation = :Stratonovich), tstops = T, callback=scb1,
+    solve(prob1, RKMilGeneral(interpretation = SciMLBase.AlgorithmInterpretation.Stratonovich), tstops = T, callback=scb1,
           save_everystep=false, save_start=false)
     avg2 .+= out1.saveval ./ Ntraj
 
     out1 = SavedValues(Float64,ComplexF64)
     scb1 = SavingCallback(fout, out1, saveat=T, save_everystep=false, save_start=false)
 
-    solve(prob2, RKMilGeneral(interpretation = :Stratonovich), tstops = T, callback=scb1,
+    solve(prob2, RKMilGeneral(interpretation = SciMLBase.AlgorithmInterpretation.Stratonovich), tstops = T, callback=scb1,
           save_everystep=false, save_start=false)
     avg3 .+= out1.saveval ./ Ntraj
   end
diff --git a/test/reversal_tests.jl b/test/reversal_tests.jl
index 06116faf0..9164d99d0 100644
--- a/test/reversal_tests.jl
+++ b/test/reversal_tests.jl
@@ -3,6 +3,7 @@ using Random
 using SDEProblemLibrary
 # automatically construct SDE transformation for Ito reversal
 using ModelingToolkit
+import SciMLBase
 
 # tested solvers
 additive_noise_solver = [
@@ -19,14 +20,14 @@ Stratonovich_solver = [
   # non-stiff methods
   EulerHeun(),
   LambaEulerHeun(),
-  RKMil(interpretation=:Stratonovich),
-  RKMilCommute(interpretation=:Stratonovich),
-  RKMilGeneral(interpretation=:Stratonovich),
+  RKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),
+  RKMilCommute(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),
+  RKMilGeneral(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),
   # S-Rock methods
-  SROCK1(interpretation=:Stratonovich),
+  SROCK1(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),
   # stiff methods
   ImplicitEulerHeun(),
-  ImplicitRKMil(interpretation=:Stratonovich),
+  ImplicitRKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),
   ISSEulerHeun(),
 ]
 
diff --git a/test/stratonovich_convergence_tests.jl b/test/stratonovich_convergence_tests.jl
index 4b8c14e35..2e457dfb3 100644
--- a/test/stratonovich_convergence_tests.jl
+++ b/test/stratonovich_convergence_tests.jl
@@ -1,5 +1,7 @@
 using StochasticDiffEq, Test, Random, DiffEqDevTools
 using SDEProblemLibrary: prob_sde_linear_stratonovich, prob_sde_2Dlinear_stratonovich
+import SciMLBase
+
 Random.seed!(100)
 dts = 1 ./2 .^(10:-1:2) #14->7 good plot
 
@@ -22,16 +24,16 @@ sim  = test_convergence(dts,prob,ImplicitEulerHeun(theta=1),trajectories=Int(5e2
 sim  = test_convergence(dts,prob,ImplicitEulerHeun(symplectic=true),trajectories=Int(5e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.1
 
-sim = test_convergence(dts,prob,RKMil(interpretation=:Stratonovich),trajectories=Int(5e2))
+sim = test_convergence(dts,prob,RKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),trajectories=Int(5e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.2
 
-sim = test_convergence(dts,prob,RKMilGeneral(interpretation=:Stratonovich),trajectories=Int(5e2))
+sim = test_convergence(dts,prob,RKMilGeneral(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),trajectories=Int(5e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.2
 
-sim  = test_convergence(dts,prob,ImplicitRKMil(interpretation=:Stratonovich),trajectories=Int(5e2))
+sim  = test_convergence(dts,prob,ImplicitRKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),trajectories=Int(5e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.1
 
-sim  = test_convergence(dts,prob,SROCK1(interpretation=:Stratonovich),trajectories=Int(2e2))
+sim  = test_convergence(dts,prob,SROCK1(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),trajectories=Int(2e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.15
 
 sim  = test_convergence(dts,prob,KomBurSROCK2(),trajectories=Int(2e2))
@@ -61,25 +63,25 @@ sim  = test_convergence(dts,prob,ImplicitEulerHeun(symplectic=true),trajectories
 
 println("RKMils")
 
-sim = test_convergence(dts,prob,RKMil(interpretation=:Stratonovich),trajectories=Int(1e2))
+sim = test_convergence(dts,prob,RKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),trajectories=Int(1e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.2
 
-sim = test_convergence(dts,prob,RKMilGeneral(interpretation=:Stratonovich),trajectories=Int(1e2))
+sim = test_convergence(dts,prob,RKMilGeneral(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),trajectories=Int(1e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.2
 
-sim  = test_convergence(dts,prob,ImplicitRKMil(interpretation=:Stratonovich),
+sim  = test_convergence(dts,prob,ImplicitRKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),
                         trajectories=Int(1e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.1
 
-sim  = test_convergence(dts,prob,ImplicitRKMil(theta=1,interpretation=:Stratonovich),
+sim  = test_convergence(dts,prob,ImplicitRKMil(theta=1,interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),
                         trajectories=Int(1e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.1
 
-sim  = test_convergence(dts,prob,ImplicitRKMil(symplectic=true,interpretation=:Stratonovich),
+sim  = test_convergence(dts,prob,ImplicitRKMil(symplectic=true,interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),
                         trajectories=Int(1e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.1
 
-sim  = test_convergence(dts,prob,SROCK1(interpretation=:Stratonovich),trajectories=Int(1e2))
+sim  = test_convergence(dts,prob,SROCK1(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich),trajectories=Int(1e2))
 @test abs(sim.𝒪est[:l2]-1) < 0.1
 
 sim  = test_convergence(dts,prob,KomBurSROCK2(),trajectories=Int(2e2))
diff --git a/test/zerod_noise_test.jl b/test/zerod_noise_test.jl
index 9a51d7f96..652716787 100644
--- a/test/zerod_noise_test.jl
+++ b/test/zerod_noise_test.jl
@@ -1,4 +1,5 @@
 using StochasticDiffEq, Test
+import SciMLBase
 
 function f(du, u, p, t)
     du[1] = u[1]
@@ -10,16 +11,16 @@ end
 u0 = [1.0]
 prob = SDEProblem{true}(f, g, u0, (0.0, 0.1))
 
-sol_ito = solve(prob, RKMil{:Ito}())
+sol_ito = solve(prob, RKMil{SciMLBase.AlgorithmInterpretation.Ito}())
 @test length(sol_ito) < 100
 
-sol_strato = solve(prob, RKMil{:Stratonovich}(); dt=1e-2)
+sol_strato = solve(prob, RKMil{SciMLBase.AlgorithmInterpretation.Stratonovich}(); dt=1e-2)
 @test length(sol_strato) < 100
 
 sol_ito = solve(prob, RKMil())
 @test length(sol_ito) < 100
 
-sol_strato = solve(prob, RKMil(interpretation=:Stratonovich); dt=1e-2)
+sol_strato = solve(prob, RKMil(interpretation=SciMLBase.AlgorithmInterpretation.Stratonovich); dt=1e-2)
 @test length(sol_strato) < 100
 
 sol_leh = solve(prob, LambaEulerHeun())