diff --git a/spacy/tests/parser/test_nn_beam.py b/spacy/tests/parser/test_nn_beam.py
index ad0dfa7a1..45c85d969 100644
--- a/spacy/tests/parser/test_nn_beam.py
+++ b/spacy/tests/parser/test_nn_beam.py
@@ -8,6 +8,7 @@ from ...syntax.arc_eager import ArcEager
 from ...tokens import Doc
 from ...gold import GoldParse
 from ...syntax._beam_utils import ParserBeam, update_beam
+from ...syntax.stateclass import StateClass
 
 
 @pytest.fixture
@@ -27,6 +28,10 @@ def moves(vocab):
 def docs(vocab):
     return [Doc(vocab, words=['Rats', 'bite', 'things'])]
 
+@pytest.fixture
+def states(docs):
+    return [StateClass(doc) for doc in docs]
+
 @pytest.fixture
 def tokvecs(docs, vector_size):
     output = []
@@ -57,8 +62,8 @@ def vector_size():
 
 
 @pytest.fixture
-def beam(moves, docs, golds, beam_width):
-    return ParserBeam(moves, docs, golds, width=beam_width)
+def beam(moves, states, golds, beam_width):
+    return ParserBeam(moves, states, golds, width=beam_width)
 
 @pytest.fixture
 def scores(moves, batch_size, beam_width):
@@ -80,19 +85,3 @@ def test_beam_advance(beam, scores):
 def test_beam_advance_too_few_scores(beam, scores):
     with pytest.raises(IndexError):
         beam.advance(scores[:-1])
-
-
-def test_update_beam(moves, docs, tokvecs, golds, vector_size):
-    @layerize
-    def state2vec(X, drop=0.):
-        vec = numpy.ones((X.shape[0], vector_size), dtype='f')
-        return vec, None
-    @layerize
-    def vec2scores(X, drop=0.):
-        scores = numpy.ones((X.shape[0], moves.n_moves), dtype='f')
-        return scores, None
-    d_loss, backprops = update_beam(moves, 13, docs, tokvecs, golds,
-                            state2vec, vec2scores, drop=0.0, sgd=None,
-                            losses={}, width=4, density=0.001)
-
-