diff --git a/src/ReliabilityOptimization.jl b/src/ReliabilityOptimization.jl
index 336b472..4188f63 100644
--- a/src/ReliabilityOptimization.jl
+++ b/src/ReliabilityOptimization.jl
@@ -16,7 +16,7 @@ end
 struct FORM{M}
     method::M
 end
-struct RIA{A, O}
+struct RIA{A,O}
     optim_alg::A
     optim_options::O
 end
@@ -43,12 +43,7 @@ function get_forward(f, p, method::FORM{<:RIA})
         n = size(p)[1]
         addvar!(innerOptModel, fill(-Inf, n + 1), fill(Inf, n + 1))
         add_eq_constraint!(innerOptModel, constr)
-        result = optimize(
-            innerOptModel,
-            alg,
-            [mean(p); 0.0],
-            options = options,
-        )
+        result = optimize(innerOptModel, alg, [mean(p); 0.0], options = options)
         return vcat(result.minimizer, result.problem.mult_g[1])
     end
     return forward
@@ -59,11 +54,7 @@ function get_conditions(f, ::FORM{<:RIA})
         p = pcmult[1:end-2]
         c = pcmult[end-1]
         mult = pcmult[end]
-        return vcat(
-            2 * p + vec(_jacobian(f, x, p)) * mult,
-            2c - mult,
-            f(x, p) .- c,
-        )
+        return vcat(2 * p + vec(_jacobian(f, x, p)) * mult, 2c - mult, f(x, p) .- c)
     end
 end
 
@@ -111,8 +102,13 @@ function (f::RandomFunction)(x)
 end
 
 # necessary type piracy FiniteDifferences._estimate_magnitudes uses this constructor which Zygote struggles to differentiate on its own
-function ChainRulesCore.rrule(::typeof(StaticArraysCore.SVector{3}), x1::T, x2::T, x3::T) where {T}
+function ChainRulesCore.rrule(
+    ::typeof(StaticArraysCore.SVector{3}),
+    x1::T,
+    x2::T,
+    x3::T,
+) where {T}
     StaticArraysCore.SVector{3}(x1, x2, x3), Δ -> (NoTangent(), Δ[1], Δ[2], Δ[3])
 end
 
-end
\ No newline at end of file
+end
diff --git a/test/example.jl b/test/example.jl
index 14442ed..19ed83f 100644
--- a/test/example.jl
+++ b/test/example.jl
@@ -1,4 +1,5 @@
-using ReliabilityOptimization, Test, NonconvexTOBS, ChainRulesCore, TopOpt, Zygote, FiniteDifferences
+using ReliabilityOptimization,
+    Test, NonconvexTOBS, ChainRulesCore, TopOpt, Zygote, FiniteDifferences
 
 const densities = [0.0, 0.5, 1.0] # for mass calculation
 const nmats = 3 # number of materials
@@ -22,11 +23,11 @@ logEs = MvNormal(log.(avgEs), Matrix(Diagonal(0.1 .* abs.(log.(avgEs)))))
 # 'Original' function. At least one input is random.
 # In this example, Es is the random input.
 function uncertainComp(x, logEs)
-  Es = exp.(logEs)
-  # interpolation of properties between materials
-  interp = MaterialInterpolation(Es, penalty)
-  MultiMaterialVariables(x, nmats) |> interp |> filter |> comp
-  # return sum(x) + sum(Es)
+    Es = exp.(logEs)
+    # interpolation of properties between materials
+    interp = MaterialInterpolation(Es, penalty)
+    MultiMaterialVariables(x, nmats) |> interp |> filter |> comp
+    # return sum(x) + sum(Es)
 end
 # wrap original function in RandomFunction struct
 rf = RandomFunction(uncertainComp, logEs, FORM(RIA()))
@@ -41,8 +42,8 @@ constr = x -> begin
     return sum(element_densities(ρs, densities)) / ncells - 0.3 # unit element volume
 end
 function obj(x) # objective for TO problem
-  dist = rf(x)
-  mean(dist)[1] + 2 * sqrt(cov(dist)[1, 1])
+    dist = rf(x)
+    mean(dist)[1] + 2 * sqrt(cov(dist)[1, 1])
 end
 obj(x0)
 Zygote.gradient(obj, x0)
@@ -51,4 +52,4 @@ FiniteDifferences.grad(central_fdm(5, 1), obj, x0)[1]
 m = Model(obj) # create optimization model
 addvar!(m, zeros(length(x0)), ones(length(x0))) # setup optimization variables
 Nonconvex.add_ineq_constraint!(m, constr) # setup volume inequality constraint
-@time r = Nonconvex.optimize(m, TOBSAlg(), x0; options = TOBSOptions()) 
+@time r = Nonconvex.optimize(m, TOBSAlg(), x0; options = TOBSOptions())
diff --git a/test/runtests.jl b/test/runtests.jl
index d218be9..305134f 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -2,10 +2,7 @@ using ReliabilityOptimization, Test, FiniteDifferences, Zygote
 
 @testset "ReliabilityOptimization.jl" begin
     # test function - y is random
-    for pol in [
-        (x, y) -> [norm(x + y)^2],
-        (x, y) -> norm(x + y)^2
-    ]
+    for pol in [(x, y) -> [norm(x + y)^2], (x, y) -> norm(x + y)^2]
         # input value to be used in example
         x = [2.0, 3.0, 6.0]
         # wrap original function in RandomFunction struct