I'm new to Haskell. I'm testing a simple function with Test.Framework:
import Test.Framework (defaultMain, testGroup)
import Test.Framework.Providers.HUnit
import Test.Framework.Providers.QuickCheck2 (testProperty)
import Test.QuickCheck
import Test.HUnit
data Kind = Variable
| Const
| Polymorphic
deriving (Show, Eq, Ord)
calculate :: Int -> Kind -> Float
calculate quantity Variable =
(**2) . fromIntegral $ quantity
calculate _ Const =
10
calculate quantity Polymorphic =
if quantity <= 10 then
10
else
(**2) . fromIntegral $ quantity
prop_ValuePositive quantity kind =
calculate quantity kind
>= 0.0
test_ValueVariable1 =
calculate 1 Variable
#?= (**2) 1
test_ValueVariable2 =
calculate 10 Variable
#?= (**2) 10
test_ValueConst1 =
calculate 1 Const
#?= 10
test_ValueConst2 =
calculate 10 Const
#?= 10
test_ValuePolymorphic1 =
calculate 1 Polymorphic
#?= 10
test_ValuePolymorphic2 =
calculate 11 Polymorphic
#?= (**2) 11
instance Test.QuickCheck.Arbitrary Kind where
arbitrary = Test.QuickCheck.oneof(
[return Variable,
return Const,
return Polymorphic])
main = defaultMain tests
tests = [
testGroup "Value" [
testProperty "Value is positive" prop_ValuePositive,
testCase "Value is calculated right for Variable"
test_ValueVariable1,
testCase "Value is calculated right for Variable"
test_ValueVariable2,
testCase "Value is calculated right for Const"
test_ValueConst1,
testCase "Value is calculated right for Const"
test_ValueConst2,
testCase "Value is calculated right for Polymorphic"
test_ValuePolymorphic1,
testCase "Value is calculated right for Polymorphic"
test_ValuePolymorphic2
]
]
What bothers me is that it's recommended to test pure functions with QuickCheck properties and impure functions with HUnit test cases. But that way, I would have to just repeat the function definition for each of 3 cases (Const, Variable and Polymorphic) in properties to test that the function returns what it's supposed to. That is too much duplication in my opinion:
prop_ValueVariable quantity Variable =
calculate quantity Variable
== ((**2) . fromIntegral $ quantity)
(and so on for all the cases of Kind)
In contrast, in the current code I test only one "obvious" property of function and provide some "sample points" for what the function should return, without actually duplicating the definition (in spirit of unit testing).
What is right approach?
Use properties for testing of all aspects of this function and possibly duplicate its definition in tests
Use properties only for, well, "properties" of what should be returned, but don't duplicate the definition and provide just some unit tests
That property based testing is for pure code and unit tests for impure code is a useful guideline, but not an absolute truth. Unit tests can be useful for pure code, too. I usually start with a unit test, e.g.
describe "parseMarkdown" $ do
it "parses links" $ do
parseMarkdown "[foo](http://foo.com/)" `shouldBe` Link "http://foo.com" "foo"
and then later abstract it to a property
it "parses *arbitrary* links" $
property $ \link#(Link url name) ->
parseMarkdown "[" ++ name ++ "](" ++ url ++ ")" `shouldBe` link
But sometimes I just stick with the unit test because either (a) there is no good property or (b) a property does not increase the test coverage.
On the other hand properties can be useful for impure code, too. You e.g. may want to test your database abstraction with properties
describe "loadUser" $ do
it "retrieves saved users from the database" $ do
property $ \user -> do
saveUser user >>= loadUser `shouldReturn` user
No, of course you are not supposed to duplicate the definition in this way. What would be the point? You may as well simplify the test to prop_trivial q k = calculate q k == calculate q k. The only case when I'd consider it is when you plan to change the way function is calculated in the future and want to check that it still returns the same result.
But if your unit tests are created by just putting values into the function definition and seeing what comes out, they are also not particularly useful either, for the same reason.
Related
Let's say there is a function to determine if a button should be visible.
fun isButtonVisible(fitlers: List<Filters>, results: List<Shop>, isLoading: Boolean) {
return fitlers.isNotEmpty() && results.isEmpty() && !isLoading
}
Now I would like to test this function using PBT like:
"the button should be visible if filters is not empty and results is empty and is not loading" {
forAll { filters: List<Filters>, results: List<Shop>, isLoading: Boolean ->
val actual = isButtonVisible(filters, results, isLoading)
// Here reimplement the logic
val expected = filters.isNotEmpty() && results.isEmpty() && !isLoading
assertThat(actual).isEqual(expected)
}
}
It seems that I just reimplement the logic again in my test, is this correct? If not, how can I come up with another properties if the logic is just simple combinations of several flags?
that is not right.
you should not have to calculate what the expected value should be during the test, you should know what the result should be, set it as such and compare it against the actual result.
Tests work by calling the method you want to test and comparing the result against an already known, expected value.
"the button should be visible when filters are not empty, results is empty, isLoading is false " {
forAll { filters: List<Filters>, results: List<Shop>, isLoading: Boolean ->
val actualVisibleFlag = isButtonVisible(filters, results, isLoading)
val expectedVisibleFlag = true
assertThat(actualVisibleFlag ).isEqual(expectedVisibleFlag )
}
}
Your expected value is known, this is the point I am trying to make.
For each combination of inputs, you create a new test.
The idea here is that when you have a bug you can easily see which existing test fails or you can add a new one which highlights the bug.
If you call a method, to give you the result of what you think you should get, well, how do you know that method is correct anyway? How do you know it works correctly for every combination?
You might get away with less tests if you reduce your number of flags maybe, do you really need 4 of them?
Now, each language / framework has ( or should have ) support for a matrix kind of thing so you can easily write the values of every combination
Another question is if there is any better way to write this method?
Public decimal CalculateTotalPrice(List<product> items)
{
decimal totalPrice = 0.m;
foreach(Product p in items)
{
if(p.Offer == "")
calc = new DefaultCalc();
else if(p.Offer == "BuyOneGetOneFree")
calc = new BuyOneGetOneFreeCalc();
else if(p.Offer == "ThreeInPriceOfTwo")
calc = new ThreeInPriceOfTwoCalc()
totalPrice += calc.Calculate(p.Quantity, p.UnitPrice);
}
return totalPrice;
}
You should probably review Polly Want a Message, by Sandi Metz
How to unit test a method that is having multiple object creation in switch statement?
An important thing to notice here is that the switch statement is an implementation detail. From the point of view of the caller, this thing is just a function
Public decimal CalculateTotalPrice(List<product> items);
If the pricing computations are fixed, you can just use the usual example based tests:
assertEquals(expectedPrice, CalculateTotalPrice(items));
But if they aren't fixed, you can still do testing based on the properties of the method. Scott Wlaschin has a really good introduction to property based testing. Based on the logic you show here, there are some things we can promise about prices, without knowing anything about the strategies in use
the price should always be greater than zero.
the price of a list of items is the same as the sum of the prices of the individual items.
if there is any better way to write this method?
You might separate choosing the pricing strategy from using the strategy. As Sandi notes, that sort of construct often shows up in more than once place.
foreach(Product p in items)
{
calc = pricing(p.Offer);
totalPrice += calc.Calculate(p.Quantity, p.UnitPrice);
}
"pricing" would then become something that you pass into this function (either as an argument, or as a dependency).
In effect, you would end up with three different kinds of test.
Checks that pricing returns the right pricing strategy for each offer.
Checks that each strategy performs its own calculation correctly.
Checks that CalculateTotalPrice compute the sum correctly.
Personally, I prefer to treat the test subject as a single large black box, but there are good counter arguments. Horses for courses.
Constructors can not be mocked (at least with free mocking frameworks).
Write tests without mocking as far as your tests run fast and test case setup is not very very complicated.
In your particular case you should be able to write tests without mocking.
Prepare data
var products = new List<Product>
{
new Product { Quantity = 10, UnitPrice = 5.0m, Offer = "" },
new Product { Quantity = 2, UnitPrice = 3.0m , Offer = "BuyOneGetOneFree" },
new Product { Quantity = 3, UnitPrice = 2.0m , Offer = "ThreeInPriceOfTwo" },
}
// prepare expected total
var expected = 57.0m; // 10 * 50.0 + 1 * 3.0 + 2 * 2.0
// Run the test
var actual = CalculateTotalPrice(products);
actual.Should().Be(expected); // pass Ok.
With this approach tests will not depend on implementation details.
You will be able to freely play with designs without rewriting tests every time you change your implementation logic.
The other answers are technically fine, but I would suggest one thing:
if(p.Offer == "")
calc = new DefaultCalc();
else if(p.Offer == "BuyOneGetOneFree")
calc = new BuyOneGetOneFreeCalc();
else if(p.Offer == "ThreeInPriceOfTwo")
calc = new ThreeInPriceOfTwoCalc()
should absolutely go into its own method/scope/whatever.
You are mapping a string to a specific calculator. That should happen in one place, and one place only. You see, first you do that here. Then some method method comes around that needs the same mapping. So you start duplicating.
How do I write a simple ABAP Unit Assert statement to check if any call, expression or other statement evaluates to true?
I can't see any basic assert() or assert_true() methods in CL_AUNIT_ASSERT while I'd expect those to be very common. I can approximate such an assert as follows, but is there no cleaner way?
cl_aunit_assert=>assert_equals(
act = boolc( lv_value > 100 OR lv_value < 2 )
exp = abap_true ).
cl_aunit_assert=>assert_equals(
act = mo_model->is_active )
exp = abap_true ).
Depending on your SAP NetWeaver stack you can (or should) use the updated ABAP Unit Class CL_ABAP_UNIT_ASSERT. This class is available at a Basis-Release >= 7.02. SAP declared this class as 'FINAL' so it´s impossible to inherit from it, but on the other side they added some ASSERT-Methods like the ASSERT_TRUE Method!
Here is a possible usage of this method:
cl_abap_unit_assert=>assert_true(
exporting
act = m_ref_foo->is_bar( l_some_var )
msg = 'is_bar Method fails with Input { l_some_var }'
).
For the releases I have access to, there's probably no shorter way than the one you outlined. You can create a subclass of CL_AUNIT_ASSERT and add your own static ASSERT_TRUE method. It's not a bad idea to do so and at the same time make your local ABAP Unit test class a subclass of that ZCL_AUNIT_ASSERT - this way, you can omit the cl_aunit_assert=> prefix which will save some keystrokes.
You cannot see such methods because there is no boolean type in ABAP.
While in Java, C++ or C, you are able to assign a result of a condition to a variable, like this
int i = 5;
boolean result = i > 3;
You cannot do the same thing in ABAP as there is no boolean type. Therefore what in other languages is a one liner, in ABAP it will always be more prolix.
DATA: i TYPE i VALUE 5.
DATA: result TYPE abap_bool.
IF i > 3.
result = abap_true.
ELSE.
result = abap_false.
ENDIF.
The thing you used seems to be a new feature, that has been recently added to the language and most of the customers will not be using for a long time. Also the CL_AUNIT_ASSERT class was created way before the new elements came to the language.
So right now, there is a possibility to write the above thing as one liner. However there is still no boolean type in the language.
DATA: i TYPE i VALUE 5.
DATA: result TYPE abap_bool.
result = boolc( i > 3 ).
On the other hand, there is no boolean type, but you could simply use ASSERT_INITIAL or ASSERT_NOT_INITIAL in this case, as boolean is emulated by either X (true) or space (false). The latter is an initial value in ABAP.
The cleanest way is to just fail:
if value > limit.
cl_abap_unit_assert=>fail( ).
endif.
Or a more informative:
cl_abap_unit=>fail( msg = 'Limit exceeded' ).
I'm interested in using SymPy to augment my engineering models. Instead of defining a rigid set of inputs and outputs, I'd like for the user to simply provide everything they know about a system, then apply that data to an algebraic model which calculates the unknowns (if it has enough data).
For an example, say I have some stuff which has some mass, volume, and density. I'd like to define a relationship between those parameters (density = mass / volume) such that when the user has provided enough information (any 2 variables), the 3rd variable is automatically calculated. Finally, if any value is later updated, one of the other values should change to preserve the relationship. One of the challenges with this system is that when there are multiple independent variables, there would need to be a way to specify which independent variable should change to satisfy the requirement.
Here's some working code that I have currently:
from sympy import *
class Stuff(object):
def __init__(self, *args, **kwargs):
#String of variables
varString = 'm v rho'
#Initialize Symbolic variables
m, v, rho = symbols(varString)
#Define density equation
# "rho = m / v" becomes "rho - m / v = 0" which means the eqn. is rho - m / v
# This is because solve() assumes equation = 0
density_eq = rho - m / v
#Store the equation and the variable string for later use
self._eqn = density_eq
self._varString = varString
#Get a list of variable names
variables = varString.split()
#Initialize parameters dictionary
self._params = {name: None for name in variables}
#property
def mass(self):
return self._params['m']
#mass.setter
def mass(self, value):
param = 'm'
self._params[param] = value
self.balance(param)
#property
def volume(self):
return self._params['v']
#volume.setter
def volume(self, value):
param = 'v'
self._params[param] = value
self.balance(param)
#property
def density(self):
return self._params['rho']
#density.setter
def density(self, value):
param = 'rho'
self._params[param] = value
self.balance(param)
def balance(self, param):
#Get the list of all variable names
variables = self._varString.split()
#Get a copy of the list except for the recently changed parameter
others = [name for name in variables if name != param]
#Loop through the less recently changed variables
for name in others:
try:
#Symbolically solve for the current variable
eq = solve(self._eqn, [symbols(name)])[0]
#Get a dictionary of variables and values to substitute for a numerical solution (will contain None for unset values)
indvars = {symbols(n): self._params[n] for n in variables if n is not name}
#Set the parameter with the new numeric solution
self._params[name] = eq.evalf(subs=indvars)
except Exception, e:
pass
if __name__ == "__main__":
#Run some examples - need to turn these into actual tests
stuff = Stuff()
stuff.density = 0.1
stuff.mass = 10.0
print stuff.volume
stuff = Water()
stuff.mass = 10.0
stuff.volume = 100.0
print stuff.density
stuff = Water()
stuff.density = 0.1
stuff.volume = 100.0
print stuff.mass
#increase volume
print "Setting Volume to 200"
stuff.volume = 200.0
print "Mass changes"
print "Mass {0}".format(stuff.mass)
print "Density {0}".format(stuff.density)
#Setting Mass to 15
print "Setting Mass to 15.0"
stuff.mass = 15.0
print "Volume changes"
print "Volume {0}".format(stuff.volume)
print "Density {0}".format(stuff.density)
#Setting Density to 0.5
print "Setting Density to 0.5"
stuff.density = 0.5
print "Mass changes"
print "Mass {0}".format(stuff.mass)
print "Volume {0}".format(stuff.volume)
print "It is impossible to let mass and volume drive density with this setup since either mass or volume will " \
"always be recalculated first."
I tried to be as elegant as I could be with the overall approach and layout of the class, but I can't help but wonder if I'm going about it wrong - if I'm using SymPy in the wrong way to accomplish this task. I'm interested in developing a complex aerospace vehicle model with dozens/hundreds of interrelated properties. I'd like to find an elegant and extensible way to use SymPy to govern property relationships across the vehicle before I scale up from this fairly simple example.
I'm also concerned about how/when to rebalance the equations. I'm familiar with PyQt Signals and Slots which is my first thought for how to link dependent things together to trigger a model update (emit a signal when a value updates, which will be received by rebalancing functions for each system of equations that relies on that parameter?). Yeah, I really don't know the best way to do this with SymPy. Might need a bigger example to explore systems of equations.
Here's some thoughts on where I'm headed with this project. Just using mass as an example, I would like to define the entire vehicle mass as the sum of the subsystem masses, and all subsystem masses as the sum of component masses. In addition, mass relationships will exist between certain subsystems and components. These relationships will drive the model until more concrete data is provided. So if the default ratio of fuel mass to total vehicle mass is 50%, then specifying 100lb of fuel will size the vehicle at 200lb. However if I later specify that the vehicle is actually 210lb, I would want the relationship recalculated (let it become dependent since fuel mass and vehicle mass were set more recently, or because I specified that they are independent variables or locked or something). The next problem is iteration. When circular or conflicting relationships exist in a model, the model must be iterated on to hopefully converge on a solution. This is often the case with the vehicle mass model described above. If the vehicle gets heavier, there needs to be more fuel to meet a requirement, which causes the vehicle to get even heavier, etc. I'm not sure how to leverage SymPy in these situations.
Any suggestions?
PS
Good explanation of the challenges associated with space launch vehicle design.
Edit: Changing the code structure based on goncalopp's suggestions...
class Balanced(object):
def __init__(self, variables, equationStr):
self._variables = variables
self._equation = sympify(equationStr)
#Initialize parameters dictionary
self._params = {name: None for name in self._variables}
def var_getter(varname, self):
return self._params[varname]
def var_setter(varname, self, value):
self._params[varname] = value
self.balance(varname)
for varname in self._variables:
setattr(Balanced, varname, property(fget=partial(var_getter, varname),
fset=partial(var_setter, varname)))
def balance(self, recentlyChanged):
#Get a copy of the list except for the recently changed parameter
others = [name for name in self._variables if name != recentlyChanged]
#Loop through the less recently changed variables
for name in others:
try:
eq = solve(self._equation, [symbols(name)])[0]
indvars = {symbols(n): self._params[n] for n in self._variables if n != name}
self._params[name] = eq.evalf(subs=indvars)
except Exception, e:
pass
class HasMass(Balanced):
def __init__(self):
super(HasMass, self).__init__(variables=['mass', 'volume', 'density'],
equationStr='density - mass / volume')
class Prop(HasMass):
def __init__(self):
super(Prop, self).__init__()
if __name__ == "__main__":
prop = Prop()
prop.density = 0.1
prop.mass = 10.0
print prop.volume
prop = Prop()
prop.mass = 10.0
prop.volume = 100.0
print prop.density
prop = Prop()
prop.density = 0.1
prop.volume = 100.0
print prop.mass
What this makes me want to do is use multiple inheritance or decorators to automatically assign physical inter-related properties to things. So I could have another class called "Cylindrical" which defines radius, diameter, and length properties, then I could have like class DowelRod(HasMass, Cylindrical). The really tricky part here is that I would want to define the cylinder volume (volume = length * pi * radius^2) and let that volume interact with the volume defined in the mass balance equations... such that, perhaps mass would respond to a change in length, etc. Not only is the multiple inheritance tricky, but automatically combining relationships will be even worse. This will get tricky very quickly. I don't know how to handle systems of equations yet, and it's clear with lots of parametric relationships that parameter locking or specifying independent/dependent variables will be necessary.
While I have no experience with this kind of models, and little experience with SymPy, here's some tips:
#property
def mass(self):
return self._params['m']
#mass.setter
def mass(self, value):
param = 'm'
self._params[param] = value
self.balance(param)
#property
def volume(self):
return self._params['v']
#volume.setter
def volume(self, value):
param = 'v'
self._params[param] = value
self.balance(param)
As you've noticed, you're repeating a lot of code for each variable. This is unnecessary, and since you'll have lots of variables, this eventually leads to code maintenance nightmares. You have your variables neatly arranged on varString = 'm v rho' My suggestion is to go further, and define a dictionary:
my_vars= {"m":"mass", "v":"volume", "rho":"density"}
and then add the properties and setters dynamically to the class (instead of explicitly):
from functools import partial
def var_getter(varname, self):
return self._params[varname]
def var_setter(varname, self, value):
self._params[varname] = value
self.balance(varname)
for k,v in my_vars.items():
setattr(Stuff, k, property(fget=partial(var_getter, v), fset=partial(var_setter, v)))
This way, you only need to write the getter and setter once.
If you want to have a couple of different getters, you can still use this technique. Store either the getter for each variable or the variables for each getter - whichever is more convenient.
Another thing that may be useful once equations get complex, is that you can keep equations as strings in your source:
density_eq = sympy.sympify( "rho - m / v" )
Using these two "tricks", you may even want to keep your variables and your equations defined in external text files, or maybe CSV.
Viewing your problem as a constraint-solving problem, you may also want to look at python-constraint (http://labix.org/python-constraint) in addition to SymPy.
Just realized that the python-constraint package doesn't apply here since the domain needs to be finite. If the domain were finite though, here's an illustrative example:
import constraint as cst
p = cst.Problem()
p.addVariables(['rho','m', 'v'], range(100))
p.addConstraint(lambda rho,m,v: rho * v == m, ['rho', 'm', 'v'])
p.addConstraint(lambda rho: rho == 2, ['rho']) # setting inputs; for illustration only
p.addConstraint(lambda m: m == 10, ['m'])
print p.getSolutions()
[{'m': 10, 'rho': 2, 'v': 5}]
However, since the real domain is needed here, the package doesn't apply.
Is it possible to pass a value in where block like this.
I have tried this. But it fails and gives MissingPropertyException.
And I want the name1 and name2 to be inside the method.
def "length of names #name"() {
def name1 = "Spock"
def name2 = "Java"
expect:
name.size() == length
where:
name || length
name1 || 5
name2 || 2
}
Try this:
def "test length of names"() {
expect:
name.size() == length
where:
[name,length]<<getTestData()
}
def getTestData(){
[["Ram" ,3 ] ,["Test" ,4] ]
}
Hope that helps!!!
Thanks
Test data belongs in the where block, not hard coded in the test (feature) method.
One rough way to see this is to think of the test body (excluding the where block) as a method with some number of parameters -- 2 in your case, name and length. And then realize that the where clause just provides data values to the test runner to use when invoking your test method.
Spock uses Groovy magic to transform
def "test length of names"() {
expect:
name.size() == length
where:
name | length
"Spock" | 5
"Java" | 4
}
into something roughly like
def test_length_of_names(name, length) { // note the arguments
assert name.size() == length
}
and then tells the test runner to call the test once for each row in the where clause
test_length_of_names("Spock", 5)
test_length_of_names("Java", 4)
This approach
provides a nice separation of concerns between test logic and test data
allows relatively generic tests to be reused for a wide range of inputs and edge cases
avoids duplication in test code
supports a test design style that deserves the name data-driven testing.
This explanation leaves out a few details, such as creating an instance of the spec for each test, updating the name of each test invocation and calling the various setup and cleanup methods.
See the Spock documentation for more details.