diff --git a/lib/SurrogatesSVM/test/runtests.jl b/lib/SurrogatesSVM/test/runtests.jl
index 5919c8f2..a15f1027 100644
--- a/lib/SurrogatesSVM/test/runtests.jl
+++ b/lib/SurrogatesSVM/test/runtests.jl
@@ -1,29 +1,42 @@
 using SafeTestsets
 
 @safetestset "SVMSurrogate" begin
-    using Surrogates, LIBSVM
     using SurrogatesSVM
-
-    #1D
-
-    obj_1D = x -> 2 * x + 1
-    a = 0.0
-    b = 10.0
-    x = sample(5, a, b, SobolSample())
-    y = obj_1D.(x)
-    my_svm_1D = SVMSurrogate(x, y, a, b)
-    val = my_svm_1D(5.0)
-    add_point!(my_svm_1D, 3.1, 7.2)
-    add_point!(my_svm_1D, [3.2, 3.5], [7.4, 8.0])
-
-    #ND
-    obj_N = x -> x[1]^2 * x[2]
-    lb = [0.0, 0.0]
-    ub = [10.0, 10.0]
-    x = sample(100, lb, ub, RandomSample())
-    y = obj_N.(x)
-    my_svm_ND = SVMSurrogate(x, y, lb, ub)
-    val = my_svm_ND((5.0, 1.2))
-    add_point!(my_svm_ND, (1.0, 1.0), 1.0)
-    add_point!(my_svm_ND, [(1.2, 1.2), (1.5, 1.5)], [1.728, 3.375])
+    using Surrogates
+    using LIBSVM
+    using Test
+    @testset "1D" begin
+        obj_1D = x -> 2 * x + 1
+        a = 0.0
+        b = 10.0
+        x = sample(5, a, b, SobolSample())
+        y = obj_1D.(x)
+        svm = LIBSVM.fit!(SVC(), reshape(x, length(x), 1), y)
+        my_svm_1D = SVMSurrogate(x, y, a, b)
+        val = my_svm_1D([5.0])
+        @test LIBSVM.predict(svm, [5.0;;])[1] == val
+        update!(my_svm_1D, [3.1], [7.2])
+        update!(my_svm_1D, [3.2, 3.5], [7.4, 8.0])
+        svm = LIBSVM.fit!(SVC(), reshape(my_svm_1D.x, length(my_svm_1D.x), 1), my_svm_1D.y)
+        val = my_svm_1D([3.1])
+        @test LIBSVM.predict(svm, [3.1;;])[1] == val
+    end
+    @testset "ND" begin
+        obj_N = x -> x[1]^2 * x[2]
+        lb = [0.0, 0.0]
+        ub = [10.0, 10.0]
+        x = collect.(sample(100, lb, ub, RandomSample()))
+        y = obj_N.(x)
+        svm = LIBSVM.fit!(SVC(), transpose(reduce(hcat, x)), y)
+        my_svm_ND = SVMSurrogate(x, y, lb, ub)
+        x_test = [5.0, 1.2]
+        val = my_svm_ND(x_test)
+        @test LIBSVM.predict(svm, reshape(x_test, 1, 2))[1] == val
+        update!(my_svm_ND, [[1.0, 1.0]], [1.0])
+        update!(my_svm_ND, [[1.2, 1.2], [1.5, 1.5]], [1.728, 3.375])
+        svm = LIBSVM.fit!(SVC(), transpose(reduce(hcat, my_svm_ND.x)), my_svm_ND.y)
+        x_test = [1.0, 1.0]
+        val = my_svm_ND(x_test)
+        @test LIBSVM.predict(svm, reshape(x_test, 1, 2))[1] == val
+    end
 end