Add new tests.
Emmanuelle Vargas-Gonzalez
parent
ef98c38937
commit
7b6236674c
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@ -228,7 +228,7 @@ def admiralty_credibility_to_value(scale_value):
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"""
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if scale_value == "6 - Truth cannot be judged":
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pass # TODO: Ask what happens here!
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raise ValueError("STIX Confidence value cannot be determined for %s" % scale_value) # TODO: What happens here?
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elif scale_value == "5 - Improbable":
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return 10
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elif scale_value == "4 - Doubtful":
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@ -270,7 +270,7 @@ def value_to_admiralty_credibility(confidence_value):
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ValueError: If `confidence_value` is out of bounds.
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"""
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# TODO: Ask what happens with "6 - Truth cannot be judged" !
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# TODO: Case "6 - Truth cannot be judged"
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if 19 >= confidence_value >= 0:
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return "5 - Improbable"
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elif 39 >= confidence_value >= 20:
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@ -10,9 +10,12 @@ COURSE_OF_ACTION_ID = "course-of-action--8e2e2d2b-17d4-4cbf-938f-98ee46b3cd3f"
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IDENTITY_ID = "identity--311b2d2d-f010-5473-83ec-1edf84858f4c"
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INDICATOR_ID = "indicator--01234567-89ab-cdef-0123-456789abcdef"
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INTRUSION_SET_ID = "intrusion-set--4e78f46f-a023-4e5f-bc24-71b3ca22ec29"
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LOCATION_ID = "location--a6e9345f-5a15-4c29-8bb3-7dcc5d168d64"
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MALWARE_ID = "malware--fedcba98-7654-3210-fedc-ba9876543210"
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MARKING_DEFINITION_ID = "marking-definition--613f2e26-407d-48c7-9eca-b8e91df99dc9"
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NOTE_ID = "note--0c7b5b88-8ff7-4a4d-aa9d-feb398cd0061"
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OBSERVED_DATA_ID = "observed-data--b67d30ff-02ac-498a-92f9-32f845f448cf"
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OPINION_ID = "opinion--b01efc25-77b4-4003-b18b-f6e24b5cd9f7"
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REPORT_ID = "report--84e4d88f-44ea-4bcd-bbf3-b2c1c320bcb3"
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RELATIONSHIP_ID = "relationship--00000000-1111-2222-3333-444444444444"
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THREAT_ACTOR_ID = "threat-actor--8e2e2d2b-17d4-4cbf-938f-98ee46b3cd3f"
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@ -0,0 +1,288 @@
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import pytest
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from stix2.confidence.scales import (admiralty_credibility_to_value,
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dni_to_value, none_low_med_high_to_value,
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value_to_admiralty_credibility,
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value_to_dni,
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value_to_none_low_medium_high,
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value_to_wep, value_to_zero_ten,
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wep_to_value, zero_ten_to_value)
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CONFIDENCE_ERROR_STR = "STIX Confidence value cannot be determined for %s"
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RANGE_ERROR_STR = "Range of values out of bounds: %s"
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def _between(x, val, y):
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return x >= val >= y
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def test_confidence_range_none_low_med_high():
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confidence_range = range(-1, 101)
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for val in confidence_range:
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if val < 0 or val > 100:
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with pytest.raises(ValueError) as excinfo:
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value_to_none_low_medium_high(val)
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assert str(excinfo.value) == RANGE_ERROR_STR % val
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continue
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if val == 0:
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assert value_to_none_low_medium_high(val) == "None"
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elif _between(29, val, 1):
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assert value_to_none_low_medium_high(val) == "Low"
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elif _between(69, val, 30):
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assert value_to_none_low_medium_high(val) == "Med"
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elif _between(100, val, 70):
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assert value_to_none_low_medium_high(val) == "High"
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else:
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pytest.fail("Unexpected behavior %s" % val)
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@pytest.mark.parametrize("scale_value,result", [
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("None", 0),
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("Low", 15),
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("Med", 50),
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("High", 85)
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])
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def test_confidence_scale_valid_none_low_med_high(scale_value, result):
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val = none_low_med_high_to_value(scale_value)
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assert val == result
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@pytest.mark.parametrize("scale_value", [
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"Super",
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"none",
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""
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])
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def test_confidence_scale_invalid_none_low_med_high(scale_value):
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with pytest.raises(ValueError) as excinfo:
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none_low_med_high_to_value(scale_value)
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assert str(excinfo.value) == CONFIDENCE_ERROR_STR % scale_value
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def test_confidence_range_zero_ten():
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confidence_range = range(-1, 101)
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for val in confidence_range:
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if val < 0 or val > 100:
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with pytest.raises(ValueError) as excinfo:
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value_to_zero_ten(val)
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assert str(excinfo.value) == RANGE_ERROR_STR % val
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continue
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if _between(4, val, 0):
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assert value_to_zero_ten(val) == "0"
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elif _between(14, val, 5):
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assert value_to_zero_ten(val) == "1"
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elif _between(24, val, 15):
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assert value_to_zero_ten(val) == "2"
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elif _between(34, val, 25):
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assert value_to_zero_ten(val) == "3"
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elif _between(44, val, 35):
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assert value_to_zero_ten(val) == "4"
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elif _between(54, val, 45):
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assert value_to_zero_ten(val) == "5"
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elif _between(64, val, 55):
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assert value_to_zero_ten(val) == "6"
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elif _between(74, val, 65):
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assert value_to_zero_ten(val) == "7"
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elif _between(84, val, 75):
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assert value_to_zero_ten(val) == "8"
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elif _between(94, val, 85):
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assert value_to_zero_ten(val) == "9"
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elif _between(100, val, 95):
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assert value_to_zero_ten(val) == "10"
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else:
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pytest.fail("Unexpected behavior %s" % val)
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@pytest.mark.parametrize("scale_value,result", [
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("0", 0),
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("1", 10),
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("2", 20),
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("3", 30),
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("4", 40),
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("5", 50),
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("6", 60),
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("7", 70),
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("8", 80),
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("9", 90),
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("10", 100)
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])
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def test_confidence_scale_valid_zero_ten(scale_value, result):
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val = zero_ten_to_value(scale_value)
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assert val == result
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@pytest.mark.parametrize("scale_value", [
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"11",
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8,
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""
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])
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def test_confidence_scale_invalid_zero_ten(scale_value):
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with pytest.raises(ValueError) as excinfo:
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zero_ten_to_value(scale_value)
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assert str(excinfo.value) == CONFIDENCE_ERROR_STR % scale_value
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def test_confidence_range_admiralty_credibility():
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confidence_range = range(-1, 101)
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for val in confidence_range:
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if val < 0 or val > 100:
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with pytest.raises(ValueError) as excinfo:
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value_to_admiralty_credibility(val)
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assert str(excinfo.value) == RANGE_ERROR_STR % val
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continue
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if _between(19, val, 0):
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assert value_to_admiralty_credibility(val) == "5 - Improbable"
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elif _between(39, val, 20):
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assert value_to_admiralty_credibility(val) == "4 - Doubtful"
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elif _between(59, val, 40):
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assert value_to_admiralty_credibility(val) == "3 - Possibly True"
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elif _between(79, val, 60):
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assert value_to_admiralty_credibility(val) == "2 - Probably True"
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elif _between(100, val, 80):
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assert value_to_admiralty_credibility(val) == "1 - Confirmed by other sources"
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else:
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pytest.fail("Unexpected behavior %s" % val)
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@pytest.mark.parametrize("scale_value,result", [
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("5 - Improbable", 10),
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("4 - Doubtful", 30),
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("3 - Possibly True", 50),
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("2 - Probably True", 70),
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("1 - Confirmed by other sources", 90)
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])
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def test_confidence_scale_valid_admiralty_credibility(scale_value, result):
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val = admiralty_credibility_to_value(scale_value)
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assert val == result
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@pytest.mark.parametrize("scale_value", [
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"5 - improbable",
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"6 - Truth cannot be judged",
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""
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])
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def test_confidence_scale_invalid_admiralty_credibility(scale_value):
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with pytest.raises(ValueError) as excinfo:
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admiralty_credibility_to_value(scale_value)
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assert str(excinfo.value) == CONFIDENCE_ERROR_STR % scale_value
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def test_confidence_range_wep():
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confidence_range = range(-1, 101)
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for val in confidence_range:
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if val < 0 or val > 100:
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with pytest.raises(ValueError) as excinfo:
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value_to_wep(val)
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assert str(excinfo.value) == RANGE_ERROR_STR % val
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continue
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if val == 0:
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assert value_to_wep(val) == "Impossible"
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elif _between(19, val, 1):
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assert value_to_wep(val) == "Highly Unlikely/Almost Certainly Not"
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elif _between(39, val, 20):
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assert value_to_wep(val) == "Unlikely/Probably Not"
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elif _between(59, val, 40):
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assert value_to_wep(val) == "Even Chance"
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elif _between(79, val, 60):
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assert value_to_wep(val) == "Likely/Probable"
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elif _between(99, val, 80):
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assert value_to_wep(val) == "Highly likely/Almost Certain"
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elif val == 100:
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assert value_to_wep(val) == "Certain"
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else:
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pytest.fail("Unexpected behavior %s" % val)
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@pytest.mark.parametrize("scale_value,result", [
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("Impossible", 0),
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("Highly Unlikely/Almost Certainly Not", 10),
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("Unlikely/Probably Not", 30),
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("Even Chance", 50),
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("Likely/Probable", 70),
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("Highly likely/Almost Certain", 90),
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("Certain", 100)
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])
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def test_confidence_scale_valid_wep(scale_value, result):
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val = wep_to_value(scale_value)
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assert val == result
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@pytest.mark.parametrize("scale_value", [
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"Unlikely / Probably Not",
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"Almost certain",
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""
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])
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def test_confidence_scale_invalid_wep(scale_value):
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with pytest.raises(ValueError) as excinfo:
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wep_to_value(scale_value)
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assert str(excinfo.value) == CONFIDENCE_ERROR_STR % scale_value
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def test_confidence_range_dni():
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confidence_range = range(-1, 101)
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for val in confidence_range:
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if val < 0 or val > 100:
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with pytest.raises(ValueError) as excinfo:
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value_to_dni(val)
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assert str(excinfo.value) == RANGE_ERROR_STR % val
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continue
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if _between(9, val, 0):
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assert value_to_dni(val) == "Almost No Chance / Remote"
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elif _between(19, val, 10):
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assert value_to_dni(val) == "Very Unlikely / Highly Improbable"
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elif _between(39, val, 20):
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assert value_to_dni(val) == "Unlikely / Improbable"
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elif _between(59, val, 40):
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assert value_to_dni(val) == "Roughly Even Change / Roughly Even Odds"
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elif _between(79, val, 60):
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assert value_to_dni(val) == "Likely / Probable"
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elif _between(89, val, 80):
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assert value_to_dni(val) == "Very Likely / Highly Probable"
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elif _between(100, val, 90):
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assert value_to_dni(val) == "Almost Certain / Nearly Certain"
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else:
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pytest.fail("Unexpected behavior %s" % val)
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@pytest.mark.parametrize("scale_value,result", [
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("Almost No Chance / Remote", 5),
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("Very Unlikely / Highly Improbable", 15),
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("Unlikely / Improbable", 30),
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("Roughly Even Change / Roughly Even Odds", 50),
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("Likely / Probable", 70),
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("Very Likely / Highly Probable", 85),
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("Almost Certain / Nearly Certain", 95)
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])
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def test_confidence_scale_valid_dni(scale_value, result):
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val = dni_to_value(scale_value)
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assert val == result
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@pytest.mark.parametrize("scale_value", [
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"Almost Certain/Nearly Certain",
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"Almost Certain / nearly Certain",
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""
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])
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def test_confidence_scale_invalid_none_dni(scale_value):
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with pytest.raises(ValueError) as excinfo:
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dni_to_value(scale_value)
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assert str(excinfo.value) == CONFIDENCE_ERROR_STR % scale_value
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@ -0,0 +1,68 @@
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import datetime as dt
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import pytest
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import pytz
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import stix2
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CAMPAIGN_ID = "campaign--12a111f0-b824-4baf-a224-83b80237a094"
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LANGUAGE_CONTENT_ID = "language-content--b86bd89f-98bb-4fa9-8cb2-9ad421da981d"
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TEST_CAMPAIGN = """{
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"type": "campaign",
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"id": "campaign--12a111f0-b824-4baf-a224-83b80237a094",
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"lang": "en",
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"created": "2017-02-08T21:31:22.007Z",
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"modified": "2017-02-08T21:31:22.007Z",
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"name": "Bank Attack",
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"description": "More information about bank attack"
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}"""
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TEST_LANGUAGE_CONTENT = """{
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"type": "language-content",
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"id": "language-content--b86bd89f-98bb-4fa9-8cb2-9ad421da981d",
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"created": "2017-02-08T21:31:22.007Z",
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"modified": "2017-02-08T21:31:22.007Z",
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"object_ref": "campaign--12a111f0-b824-4baf-a224-83b80237a094",
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"object_modified": "2017-02-08T21:31:22.007Z",
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"contents": {
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"de": {
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"name": "Bank Angriff 1",
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"description": "Weitere Informationen über Banküberfall"
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},
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"fr": {
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"name": "Attaque Bank 1",
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"description": "Plus d'informations sur la crise bancaire"
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}
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}
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}"""
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@pytest.mark.xfail(reason="Dictionary keys are too short")
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def test_language_content_campaign():
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now = dt.datetime(2017, 2, 8, 21, 31, 22, microsecond=7000, tzinfo=pytz.utc)
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lc = stix2.LanguageContent(
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type='language-content',
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id=LANGUAGE_CONTENT_ID,
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created=now,
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modified=now,
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object_ref=CAMPAIGN_ID,
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object_modified=now,
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contents={
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"de": {
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"name": "Bank Angriff 1",
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"description": "Weitere Informationen über Banküberfall"
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},
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"fr": {
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"name": "Attaque Bank 1",
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"description": "Plus d'informations sur la crise bancaire"
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}
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}
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)
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camp = stix2.parse(TEST_CAMPAIGN)
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assert str(lc) in TEST_LANGUAGE_CONTENT
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assert lc.modified == camp.modified
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@ -0,0 +1,82 @@
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import datetime as dt
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import re
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import pytest
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import pytz
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import stix2
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from .constants import LOCATION_ID
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EXPECTED_LOCATION_1 = """{
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"type": "location",
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"id": "location--a6e9345f-5a15-4c29-8bb3-7dcc5d168d64",
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"created": "2016-04-06T20:03:00.000Z",
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"modified": "2016-04-06T20:03:00.000Z",
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"latitude": 48.8566,
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"longitude": 2.3522
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}"""
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EXPECTED_LOCATION_1_REPR = "Location(" + " ".join("""
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type='location',
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id='location--a6e9345f-5a15-4c29-8bb3-7dcc5d168d64',
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created='2016-04-06T20:03:00.000Z',
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modified='2016-04-06T20:03:00.000Z',
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latitude=48.8566,
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longitude=2.3522""".split()) + ")"
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EXPECTED_LOCATION_2 = """{
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"type": "location",
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"id": "location--a6e9345f-5a15-4c29-8bb3-7dcc5d168d64",
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"created": "2016-04-06T20:03:00.000Z",
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"modified": "2016-04-06T20:03:00.000Z",
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"region": "north-america"
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}
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"""
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EXPECTED_LOCATION_2_REPR = "Location(" + " ".join("""
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type='location',
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id='location--a6e9345f-5a15-4c29-8bb3-7dcc5d168d64',
|
||||
created='2016-04-06T20:03:00.000Z',
|
||||
modified='2016-04-06T20:03:00.000Z',
|
||||
region='north-america'""".split()) + ")"
|
||||
|
||||
|
||||
def test_location_with_some_required_properties():
|
||||
now = dt.datetime(2016, 4, 6, 20, 3, 0, tzinfo=pytz.utc)
|
||||
|
||||
loc = stix2.Location(
|
||||
type="location",
|
||||
id=LOCATION_ID,
|
||||
created=now,
|
||||
modified=now,
|
||||
latitude=48.8566,
|
||||
longitude=2.3522
|
||||
)
|
||||
|
||||
assert str(loc) == EXPECTED_LOCATION_1
|
||||
rep = re.sub(r"(\[|=| )u('|\"|\\\'|\\\")", r"\g<1>\g<2>", repr(loc))
|
||||
assert rep == EXPECTED_LOCATION_1_REPR
|
||||
|
||||
|
||||
@pytest.mark.parametrize("data", [
|
||||
EXPECTED_LOCATION_2,
|
||||
{
|
||||
"type": "location",
|
||||
"id": "location--a6e9345f-5a15-4c29-8bb3-7dcc5d168d64",
|
||||
"created": "2016-04-06T20:03:00.000Z",
|
||||
"modified": "2016-04-06T20:03:00.000Z",
|
||||
"region": "north-america"
|
||||
}
|
||||
])
|
||||
def test_parse_location(data):
|
||||
location = stix2.parse(data)
|
||||
|
||||
assert location.type == 'location'
|
||||
assert location.id == LOCATION_ID
|
||||
assert location.created == dt.datetime(2016, 4, 6, 20, 3, 0, tzinfo=pytz.utc)
|
||||
assert location.modified == dt.datetime(2016, 4, 6, 20, 3, 0, tzinfo=pytz.utc)
|
||||
assert location.region == 'north-america'
|
||||
rep = re.sub(r"(\[|=| )u('|\"|\\\'|\\\")", r"\g<1>\g<2>", repr(location))
|
||||
assert rep == EXPECTED_LOCATION_2_REPR
|
||||
|
|
@ -0,0 +1,110 @@
|
|||
import datetime as dt
|
||||
import re
|
||||
|
||||
import pytest
|
||||
import pytz
|
||||
|
||||
import stix2
|
||||
|
||||
from .constants import CAMPAIGN_ID, NOTE_ID
|
||||
|
||||
DESCRIPTION = ('This note indicates the various steps taken by the threat'
|
||||
' analyst team to investigate this specific campaign. Step'
|
||||
' 1) Do a scan 2) Review scanned results for identified '
|
||||
'hosts not known by external intel... etc')
|
||||
|
||||
EXPECTED_NOTE = """{
|
||||
"type": "note",
|
||||
"id": "note--0c7b5b88-8ff7-4a4d-aa9d-feb398cd0061",
|
||||
"created": "2016-05-12T08:17:27.000Z",
|
||||
"modified": "2016-05-12T08:17:27.000Z",
|
||||
"summary": "Tracking Team Note#1",
|
||||
"description": "%s",
|
||||
"authors": [
|
||||
"John Doe"
|
||||
],
|
||||
"object_refs": [
|
||||
"campaign--8e2e2d2b-17d4-4cbf-938f-98ee46b3cd3f"
|
||||
],
|
||||
"external_references": [
|
||||
{
|
||||
"source_name": "job-tracker",
|
||||
"external_id": "job-id-1234"
|
||||
}
|
||||
]
|
||||
}""" % DESCRIPTION
|
||||
|
||||
EXPECTED_OPINION_REPR = "Note(" + " ".join(("""
|
||||
type='note',
|
||||
id='note--0c7b5b88-8ff7-4a4d-aa9d-feb398cd0061',
|
||||
created='2016-05-12T08:17:27.000Z',
|
||||
modified='2016-05-12T08:17:27.000Z',
|
||||
summary='Tracking Team Note#1',
|
||||
description='%s',
|
||||
authors=['John Doe'],
|
||||
object_refs=['campaign--8e2e2d2b-17d4-4cbf-938f-98ee46b3cd3f'],
|
||||
external_references=[ExternalReference(source_name='job-tracker', external_id='job-id-1234')]
|
||||
""" % DESCRIPTION).split()) + ")"
|
||||
|
||||
|
||||
def test_note_with_required_properties():
|
||||
now = dt.datetime(2016, 5, 12, 8, 17, 27, tzinfo=pytz.utc)
|
||||
|
||||
note = stix2.Note(
|
||||
type='note',
|
||||
id=NOTE_ID,
|
||||
created=now,
|
||||
modified=now,
|
||||
summary='Tracking Team Note#1',
|
||||
object_refs=[CAMPAIGN_ID],
|
||||
authors=['John Doe'],
|
||||
description=DESCRIPTION,
|
||||
external_references=[
|
||||
{
|
||||
'source_name': 'job-tracker',
|
||||
'external_id': 'job-id-1234'
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
assert str(note) == EXPECTED_NOTE
|
||||
rep = re.sub(r"(\[|=| )u('|\"|\\\'|\\\")", r"\g<1>\g<2>", repr(note))
|
||||
assert rep == EXPECTED_OPINION_REPR
|
||||
|
||||
|
||||
@pytest.mark.parametrize("data", [
|
||||
EXPECTED_NOTE,
|
||||
{
|
||||
"type": "note",
|
||||
"id": "note--0c7b5b88-8ff7-4a4d-aa9d-feb398cd0061",
|
||||
"created": "2016-05-12T08:17:27.000Z",
|
||||
"modified": "2016-05-12T08:17:27.000Z",
|
||||
"summary": "Tracking Team Note#1",
|
||||
"description": DESCRIPTION,
|
||||
"authors": [
|
||||
"John Doe"
|
||||
],
|
||||
"object_refs": [
|
||||
"campaign--8e2e2d2b-17d4-4cbf-938f-98ee46b3cd3f"
|
||||
],
|
||||
"external_references": [
|
||||
{
|
||||
"source_name": "job-tracker",
|
||||
"external_id": "job-id-1234"
|
||||
}
|
||||
]
|
||||
}
|
||||
])
|
||||
def test_parse_note(data):
|
||||
note = stix2.parse(data)
|
||||
|
||||
assert note.type == 'note'
|
||||
assert note.id == NOTE_ID
|
||||
assert note.created == dt.datetime(2016, 5, 12, 8, 17, 27, tzinfo=pytz.utc)
|
||||
assert note.modified == dt.datetime(2016, 5, 12, 8, 17, 27, tzinfo=pytz.utc)
|
||||
assert note.object_refs[0] == CAMPAIGN_ID
|
||||
assert note.authors[0] == 'John Doe'
|
||||
assert note.summary == 'Tracking Team Note#1'
|
||||
assert note.description == DESCRIPTION
|
||||
rep = re.sub(r"(\[|=| )u('|\"|\\\'|\\\")", r"\g<1>\g<2>", repr(note))
|
||||
assert rep == EXPECTED_OPINION_REPR
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
import datetime as dt
|
||||
import re
|
||||
|
||||
import pytest
|
||||
import pytz
|
||||
|
||||
import stix2
|
||||
|
||||
from .constants import OPINION_ID
|
||||
|
||||
DESCRIPTION = ('This doesn\'t seem like it is feasible. We\'ve seen how '
|
||||
'PandaCat has attacked Spanish infrastructure over the '
|
||||
'last 3 years, so this change in targeting seems too great'
|
||||
' to be viable. The methods used are more commonly '
|
||||
'associated with the FlameDragonCrew.')
|
||||
|
||||
EXPECTED_OPINION = """{
|
||||
"type": "opinion",
|
||||
"id": "opinion--b01efc25-77b4-4003-b18b-f6e24b5cd9f7",
|
||||
"created": "2016-05-12T08:17:27.000Z",
|
||||
"modified": "2016-05-12T08:17:27.000Z",
|
||||
"description": "%s",
|
||||
"object_refs": [
|
||||
"relationship--16d2358f-3b0d-4c88-b047-0da2f7ed4471"
|
||||
],
|
||||
"opinion": "strongly-disagree"
|
||||
}""" % DESCRIPTION
|
||||
|
||||
EXPECTED_OPINION_REPR = "Opinion(" + " ".join(("""
|
||||
type='opinion',
|
||||
id='opinion--b01efc25-77b4-4003-b18b-f6e24b5cd9f7',
|
||||
created='2016-05-12T08:17:27.000Z',
|
||||
modified='2016-05-12T08:17:27.000Z',
|
||||
description="%s",
|
||||
object_refs=['relationship--16d2358f-3b0d-4c88-b047-0da2f7ed4471'],
|
||||
opinion='strongly-disagree'""" % DESCRIPTION).split()) + ")"
|
||||
|
||||
|
||||
def test_opinion_with_required_properties():
|
||||
now = dt.datetime(2016, 5, 12, 8, 17, 27, tzinfo=pytz.utc)
|
||||
|
||||
opi = stix2.Opinion(
|
||||
type='opinion',
|
||||
id=OPINION_ID,
|
||||
created=now,
|
||||
modified=now,
|
||||
object_refs=['relationship--16d2358f-3b0d-4c88-b047-0da2f7ed4471'],
|
||||
opinion='strongly-disagree',
|
||||
description=DESCRIPTION
|
||||
)
|
||||
|
||||
assert str(opi) == EXPECTED_OPINION
|
||||
rep = re.sub(r"(\[|=| )u('|\"|\\\'|\\\")", r"\g<1>\g<2>", repr(opi))
|
||||
assert rep == EXPECTED_OPINION_REPR
|
||||
|
||||
|
||||
@pytest.mark.parametrize("data", [
|
||||
EXPECTED_OPINION,
|
||||
{
|
||||
"type": "opinion",
|
||||
"id": "opinion--b01efc25-77b4-4003-b18b-f6e24b5cd9f7",
|
||||
"created": "2016-05-12T08:17:27.000Z",
|
||||
"modified": "2016-05-12T08:17:27.000Z",
|
||||
"description": DESCRIPTION,
|
||||
"object_refs": [
|
||||
"relationship--16d2358f-3b0d-4c88-b047-0da2f7ed4471"
|
||||
],
|
||||
"opinion": "strongly-disagree"
|
||||
}
|
||||
])
|
||||
def test_parse_opinion(data):
|
||||
opinion = stix2.parse(data)
|
||||
|
||||
assert opinion.type == 'opinion'
|
||||
assert opinion.id == OPINION_ID
|
||||
assert opinion.created == dt.datetime(2016, 5, 12, 8, 17, 27, tzinfo=pytz.utc)
|
||||
assert opinion.modified == dt.datetime(2016, 5, 12, 8, 17, 27, tzinfo=pytz.utc)
|
||||
assert opinion.opinion == 'strongly-disagree'
|
||||
assert opinion.object_refs[0] == 'relationship--16d2358f-3b0d-4c88-b047-0da2f7ed4471'
|
||||
assert opinion.description == DESCRIPTION
|
||||
rep = re.sub(r"(\[|=| )u('|\"|\\\'|\\\")", r"\g<1>\g<2>", repr(opinion))
|
||||
assert rep == EXPECTED_OPINION_REPR
|
||||
Loading…
Reference in New Issue