Do you use constants from the implementation in your test cases? - unit-testing

Let's say you have some code like this (in a made-up langague, since it does not matter for this question):
constant float PI = 3.14;
float getPi()
{
return PI;
}
Would you test it like this:
testPiIs3point14()
{
// Test using literal in test case
AssertEquals( getPi(), 3.14 );
}
Or like this:
testPiIs3Point14()
{
// Test using constant from implementation in test case
AssertEquals( getPi(), PI );
}
In other words, do you use constants from your system under test in your test cases? Or is this considered an implementation detail?

The two tests verify/assert different purposes.
The first one (using literal in the test case)
Ensures that getPi() will always return 3.14.
It covers both the constant and the function and will fail if ever someone finds the PI value used in the software is not accurate enough and replace it with, say 3.14159.
This can be good or bad, depending on the context.
The second one (reuse the implementation code)
only covers the function.
It will not fail if someone changes the constant;
It will only fail if the function is
modified to return another constant (with a different value).
Choosing between the two depends on the objective of the test.
Use a literal if the constant must never change.
Use the constant to pin down the behavior of the function: return a
constant - whatever its value. In the second case, the test may be needless.

I was looking for information on this same topic. My conclusion so far is that you should not use literals, but you should also make sure the constant is what you expect it to be.
If you used literals in 10 different unit tests and the value changed for any reason, then you'd have to change the value in all 10 literals. You could or example need to add more precision to PI.
The best alternative IMHO is to implement a unit test to check the constant's value to be what you expect, and then use the constant freely in your other tests.
Something along the lines of implementing these two tests:
testPiIs3point14()
{
AssertEquals( PI, 3.14 );
}
testGetPiReturnsPi()
{
AssertEquals( getPi(), PI );
}
PS: While checking the value may not be so important for all constants, it could be very important for some. One example I can think of is constants that contain URLs or similar values.

I think this is the question about coupling between the tests and the production code. When I first started TDD I thought that having the constant in the tests makes the tests more thorough. However, now I think it just causes tighter coupling between the tests and the implementation. Does it make it more safe if you copy and paste the constant to the test? Not really. It just makes it more painful to change the constant, especially if its copy-pasted to multiple tests. These tests don't test if it is the right constant, they just test that this constant is returned from the method, so now I would definitely go for test number two.

If you really have both exposed as part of the public interface, it would be two tests:
testPI() {
AssertEquals(PI, 3.14);
}
test_getPi() {
AssertEquals(getPi(), 3.14);
}
However, if PI is an implementation detail, and getPi is the public means to retrieve the value, then you would only write test_getPi, as above, and PI should be private with no testPI unit test. This latter case is probably more like what you should do.
I guess you could have a third test like this:
test_getPi_PI_AlwaysAgree() {
AssertEquals(getPi(), PI);
}
This says that these two pieces of code should always evaluate to the same value, whatever that value is. If that's how your logic is supposed to work, that's how you'd test it, but more likely you shouldn't be having two identical means of doing the same thing in the first place.
Note that this last test does not say that getPi is expected to return 3.14, only that the two pieces of code should have the same value, without any indication of what that value is. To assert that the value of one or the other should be some specific value, use one of the first two tests.

I think there are two distinct cases here:
If you are testing a constant that is critical to the result of a calculation, as in your example, I think it's better to use an independent test rather than re-using the same constant from the code you are trying to test. Instead of testing the constant value directly, I would test (for example) the function CalculateAreaOfCircle(), which would verify that the Area formula is correct and at the same time verify the value of PI.
I think it makes sense to re-use enumerations and other constants that do not directly affect the outcome of critical parts of the code.

I use the first form - even though it duplicates the value (twice only), it is more readable.
[Test]
public void GetPIReturns3_14()
{
Assert.AreEqual(3.14, testSubject.GetPI());
}
The duplication is necessary because if you reuse the constant as in the second test, you are not really testing anything. In effect, you're testing "Is Constant == Constant?". This test would never fail. e.g. if I change PI to be 1010.1010, the second test wouldn't fail.

Definitely the second form, the purpose of the constant is to not introduce a "magic number". Now, in unit tests, you tend to use magic numbers quite a bit and that's OK.
In this case, you introduced a constant AND used a magic number. I would either use the constant everywhere or not use it at all.

Related

Use of Literals, yay/nay in C++

I've recently heard that in some cases, programmers believe that you should never use literals in your code. I understand that in some cases, assigning a variable name to a given number can be helpful (especially in terms of maintenance if that number is used elsewhere). However, consider the following case studies:
Case Study 1: Use of Literals for "special" byte codes.
Say you have an if statement that checks for a specific value stored in (for the sake of argument) a uint16_t. Here are the two code samples:
Version 1:
// Descriptive comment as to why I'm using 0xBEEF goes here
if (my_var == 0xBEEF) {
//do something
}
Version 2:
const uint16_t kSuperDescriptiveVarName = 0xBEEF;
if (my_var == kSuperDescriptiveVarName) {
// do something
}
Which is the "preferred" method in terms of good coding practice? I can fully understand why you would prefer version 2 if kSuperDescriptiveVarName is used more than once. Also, does the compiler do any optimizations to make both versions effectively the same executable code? That is, are there any performance implications here?
Case Study 2: Use of sizeof
I fully understand that using sizeof versus a raw literal is preferred for portability and also readability concerns. Take the two code examples into account. The scenario is that you are computing the offset into a packet buffer (an array of uint8_t) where the first part of the packet is stored as my_packet_header, which let's say is a uint32_t.
Version 1:
const int offset = sizeof(my_packet_header);
Version 2:
const int offset = 4; // good comment telling reader where 4 came from
Clearly, version 1 is preferred, but what about for cases where you have multiple data fields to skip over? What if you have the following instead:
Version 1:
const int offset = sizeof(my_packet_header) + sizeof(data_field1) + sizeof(data_field2) + ... + sizeof(data_fieldn);
Version 2:
const int offset = 47;
Which is preferred in this case? Does is still make sense to show all the steps involved with computing the offset or does the literal usage make sense here?
Thanks for the help in advance as I attempt to better my code practices.
Which is the "preferred" method in terms of good coding practice? I can fully understand why you would prefer version 2 if kSuperDescriptiveVarName is used more than once.
Sounds like you understand the main point... factoring values (and their comments) that are used in multiple places. Further, it can sometimes help to have a group of constants in one place - so their values can be inspected, verified, modified etc. without concern for where they're used in the code. Other times, there are many constants used in proximity and the comments needed to properly explain them would obfuscate the code in which they're used.
Countering that, having a const variable means all the programmers studying the code will be wondering whether it's used anywhere else, keeping it in mind as they inspect the rest of the scope in which it's declared etc. - the less unnecessary things to remember the surer the understanding of important parts of the code will be.
Like so many things in programming, it's "an art" balancing the pros and cons of each approach, and best guided by experience and knowledge of the way the code's likely to be studied, maintained, and evolved.
Also, does the compiler do any optimizations to make both versions effectively the same executable code? That is, are there any performance implications here?
There's no performance implications in optimised code.
I fully understand that using sizeof versus a raw literal is preferred for portability and also readability concerns.
And other reasons too. A big factor in good programming is reducing the points of maintenance when changes are done. If you can modify the type of a variable and know that all the places using that variable will adjust accordingly, that's great - saves time and potential errors. Using sizeof helps with that.
Which is preferred [for calculating offsets in a struct]? Does is still make sense to show all the steps involved with computing the offset or does the literal usage make sense here?
The offsetof macro (#include <cstddef>) is better for this... again reducing maintenance burden. With the this + that approach you illustrate, if the compiler decides to use any padding your offset will be wrong, and further you have to fix it every time you add or remove a field.
Ignoring the offsetof issues and just considering your this + that example as an illustration of a more complex value to assign, again it's a balancing act. You'd definitely want some explanation/comment/documentation re intent here (are you working out the binary size of earlier fields? calculating the offset of the next field?, deliberately missing some fields that might not be needed for the intended use or was that accidental?...). Still, a named constant might be enough documentation, so it's likely unimportant which way you lean....
In every example you list, I would go with the name.
In your first example, you almost certainly used that special 0xBEEF number at least twice - once to write it and once to do your comparison. If you didn't write it, that number is still part of a contract with someone else (perhaps a file format definition).
In the last example, it is especially useful to show the computation that yielded the value. That way, if you encounter trouble down the line, you can easily see either that the number is trustworthy, or what you missed and fix it.
There are some cases where I prefer literals over named constants though. These are always cases where a name is no more meaningful than the number. For example, you have a game program that plays a dice game (perhaps Yahtzee), where there are specific rules for specific die rolls. You could define constants for One = 1, Two = 2, etc. But why bother?
Generally it is better to use a name instead of a value. After all, if you need to change it later, you can find it more easily. Also it is not always clear why this particular number is used, when you read the code, so having a meaningful name assigned to it, makes this immediately clear to a programmer.
Performance-wise there is no difference, because the optimizers should take care of it. And it is rather unlikely, even if there would be an extra instruction generated, that this would cause you troubles. If your code would be that tight, you probably shouldn't rely on an optimizer effect anyway.
I can fully understand why you would prefer version 2 if kSuperDescriptiveVarName is used more than once.
I think kSuperDescriptiveVarName will definitely be used more than once. One for check and at least one for assignment, maybe in different part of your program.
There will be no difference in performance, since an optimization called Constant Propagation exists in almost all compilers. Just enable optimization for your compiler.

Testing a function: what more should be tested?

I am writing a function that takes three integer inputs and based on a relation between the three, it returns a value or error. To test this, I have written some test cases which include testing illegal values, boundary conditions for integers including overflows and some positive tests too. I am wondering what else should be tested for this simple function?
Can testing on different platforms make sense as a test case for such a small function?
Also, testing execution times is another thing that I wanted to add as a test case.
Can doing static and dynamic analysis be a part of the test cases?
Anything else that should be tested?
int foo(int a, int b, int c) {
return a value based on a, b, and c.
}
The way you ask your question it seems you are doing a black box test, i.e. you only know about the relation between input and output, and not about the implementation. In that case your test case should depend on what you know about the relation, and I think you have tested these things (you didn't give us details on the relation).
From that it doesn't look as if you need to test for platform independence, but if you have an automated test suite, it is for sure not a bad idea to test it on different platforms.
Now if you have the code available, you could go for white box tests. Typically you would do this by looking at your code structure first, i.e. you could try to have 100% branching coverage, i.e. every branch in your code is at least run once during the tests. In that way, static and dynamic analysis could help you to find different coverage measures.
I wouldn't go for a platform independency test if there is no platform dependent code in your function.
sizeof(int) must be tested for the particular compiler. Although this seems trivial and C standard specifies the size for an int, its always better to know if the compiler being used is a 16 bit standard-noncomformant compiler. Just another test case.

C++ test to verify equality operator is kept consistent with struct over time

I voted up #TomalakGeretkal for a good note about by-contract; I'm haven't accepted an answer as my question is how to programatically check the equals function.
I have a POD struct & an equality operator, a (very) small part of a system with >100 engineers.
Over time I expect the struct to be modified (members added/removed/reordered) and I want to write a test to verify that the equality op is testing every member of the struct (eg is kept up to date as the struct changes).
As Tomalak pointed out - comments & "by contract" is often the best/only way to enforce this; however in my situation I expect issues and want to explore whether there are any ways to proactively catch (at least many) of the modifications.
I'm not coming up with a satisfactory answer - this is the best I've thought of:
-new up two instances struct (x, y), fill each with identical non-zero data.
-check x==y
-modify x "byte by byte"
-take ptr to be (unsigned char*)&x
-iterator over ptr (for sizeof(x))
-increment the current byte
-check !(x==y)
-decrement the current byte
-check x==y
The test passes if the equality operator caught every byte (NOTE: there is a caveat to this - not all bytes are used in the compilers representation of x, therefore the test would have to 'skip' these bytes - eg hard code ignore bytes)
My proposed test has significant problems: (at least) the 'don't care' bytes, and the fact that incrementing one byte of the types in x may not result in a valid value for the variable at that memory location.
Any better solutions?
(This shouldn't matter, but I'm using VS2008, rtti is off, googletest suite)
Though tempting to make code 'fool-proof' with self-checks like this, it's my experience that keeping the self-checks themselves fool-proof is, well, a fool's errand.
Keep it simple and localise the effect of any changes. Write a comment in the struct definition making it clear that the equality operator must also be updated if the struct is; then, if this fails, it's just the programmer's fault.
I know that this will not seem optimal to you as it leaves the potential for user error in the future, but in reality you can't get around this (at least without making your code horrendously complicated), and often it's most practical just not to bother.
I agree with (and upvoted) Tomalak's answer. It's unlikely that you'll find a foolproof solution. Nonetheless, one simple semi-automated approach could be to validate the expected size within the equality operator:
MyStruct::operator==(const MyStruct &rhs)
{
assert(sizeof(MyStruct) == 42); // reminder to update as new members added
// actual functionality here ...
}
This way, if any new members are added, the assert will fire until someone updates the equality operator. This isn't foolproof, of course. (Member vars might be replaced with something of same size, etc.) Nonetheless, it's a relatively simple (one line assert) that has a good shot of detecting the error case.
I'm sure I'm going to get downvoted for this but...
How about a template equality function that takes a reference to an int parameter, and the two objects being tested. The equality function will return bool, but will increment the size reference (int) by the sizeof(T).
Then have a large test function that calls the template for each object and sums the total size --> compare this sum with the sizeof the object. The existence of virtual functions/inheritance, etc could kill this idea.
it's actually a difficult problem to solve correctly in a self-test.
the easiest solution i can think of is to take a few template functions which operate on multiple types, perform the necessary conversions, promotions, and comparisons, then verify the result in an external unit test. when a breaking change is introduced, at least you'll know.
some of these challenges are more easily maintained/verified using approaches such as composition, rather than extension/subclassing.
Agree with Tomalak and Eric. I have used this for very similar problems.
Assert does not work unless the DEBUG is defined, so potentially you can release code that is wrong. These tests will not always work reliably. If the structure contains bit fields, or items are inserted that take up slack space cause by compiler aligning to word boundaries, the size won't change. For this reason they offer limited value. e.g.
struct MyStruct {
char a ;
ulong l ;
}
changed to
struct MyStruct {
char a ;
char b ;
ulong l ;
}
Both structures are 8 bytes (on 32bit Linux x86)

should unit tests be black box tests or white box tests?

Say I have three methods, all very similar but with different input types:
void printLargestNumber(int a, int b) { ... }
void printLargestNumber(double a, double b) { ... }
void printLargestNumber(String numberAsString, String numberAsString) { ... }
All three use the same underlying logic. For example: maybe the double version is the only one that compares numbers, and the other two just convert their inputs to double.
We could imagine a few different unit tests: first input is larger, second is larger, both inputs are negative, etc.
My Question
Should all three methods have the full set of tests (black box since we don't assume the core implementation is the same)
or
Should only the double version be tested heavily and the other two tested lightly to verify parameter conversion (white box testing since we know they share the same implementation and it's already been tested in the double tests)?
If all of those methods are public, i.e. callable by the outside world, I'd definitely test all of them with a full set of tests. One good reason is that white-box tests are more brittle than black-box tests; if the implementation changes the public contract might change for some of those methods.
There are a set of tests that explicitly exercise the public interfaces. I would treat those as black-box tests.
There are a second set of tests that could be seen as looking at the corner cases of the implementation. This is white box testing and surely has a place in a Unit test. You can't know the interesting paths without some white-box implementation knowledge. I would pay particular attention to the String case, because the interface allows for strings that may not convert cleanly to doubles, that push the boundaries of precision etc.
Would I cut a few corners in the integer case? I know I pushed the paths in the double case, probably shouldn't but might well under time pressure.
It depends.
Do you think the implementation is likely to change? If so then go with black box testing.
If you can guarantee that the implementation won't change go with white box. However, the chances of you being able to guarantee this aren't 100%.
You could compromise and do some of the black box tests, particularly around the boundary conditions. However, writing the tests should be easy - so there's no excuse from that point of view for not doing full black box testing. The only limiting factor is the time it takes to run the tests.
Perhaps you should investigate the possibility of running the tests in parallel.

What type of errors could my code still contain even if I have 100% code coverage?

What type of errors could my code still contain even if I have 100% code coverage? I'm looking for concrete examples or links to concrete examples of such errors.
Having 100% code coverage is not that great as one may think of it. Consider a trival example:
double Foo(double a, double b)
{
return a / b;
}
Even a single unit test will raise code coverage of this method to 100%, but the said unit test will not tell us what code is working and what code is not. This might be a perfectly valid code, but without testing edge conditions (such as when b is 0.0) unit test is inconclusive at best.
Code coverage only tells us what was executed by our unit tests, not whether it was executed correctly. This is an important distinction to make. Just because a line of code is executed by a unit test, does not necessarily mean that that line of code is working as intended.
Listen to this for an interesting discussion.
Code coverage doesn't mean that your code is bug free in any way. It's an estimate on how well you're test cases cover your source code base. 100% code coverage would imply that every line of code is tested but every state of your program certainly is not. There's research being done in this area, I think it's referred to as finite state modeling but it's really a brute force way of trying to explore every state of a program.
A more elegant way of doing the same thing is something referred to as abstract interpretation. MSR (Microsoft Research) have released something called CodeContracts based on abstract interpretation. Check out Pex as well, they really emphasis cleaver methods of testing application run-time behavior.
I could write a really good test which would give me good coverage, but there's no guarantees that that test will explore all the states that my program might have. This is the problem of writing really good tests, which is hard.
Code coverage does not imply good tests
Uh? Any kind of ordinary logic bug, I guess? Memory corruption, buffer overrun, plain old wrong code, assignment-instead-of-test, the list goes on. Coverage is only that, it lets you know that all code paths are executed, not that they are correct.
As I haven't seen it mentioned yet, I'd like to add this thread that code coverage does not tell you what part of your code is bugfree.
It only tells you what parts of your code is guaranteed to be untested.
1. "Data space" problems
Your (bad) code:
void f(int n, int increment)
{
while(n < 500)
{
cout << n;
n += increment;
}
}
Your test:
f(200,100);
Bug in real world use:
f(200,0);
My point: Your test may cover 100% of the lines of your code but it will not (typically) cover all your possible input data space, i.e. the set of all possible values of inputs.
2. Testing against your own mistake
Another classical example is when you just take a bad decision in design, and test your code against your own bad decision.
E.g. The specs document says "print all prime numbers up to n" and you print all prime numbers up to n but excluding n. And your unit tests test your wrong idea.
3. Undefined behaviour
Use the value of uninitialized variables, cause an invalid memory access, etc. and your code has undefined behaviour (in C++ or any other language that contemplates "undefined behaviour"). Sometimes it will pass your tests, but it will crash in the real world.
...
There can always be runtime exceptions: memory filling up, database or other connections not being closed etc...
Consider the following code:
int add(int a, int b)
{
return a + b;
}
This code could fail to implement some necessary functionality (i.e. not meet an end-user requirements): "100% coverage" doesn't necessarily test/detect functionality which ought to be implemented but which isn't.
This code could work for some but not all input data ranges (e.g. when a and b are both very large).
Code coverage doesn't mean anything, if your tests contain bugs, or you are testing the wrong thing.
As a related tangent; I'd like to remind to you that I can trivially construct an O(1) method that satisfies the following pseudo-code test:
sorted = sort(2,1,6,4,3,1,6,2);
for element in sorted {
if (is_defined(previousElement)) {
assert(element >= previousElement);
}
previousElement = element;
}
bonus karma to Jon Skeet, who pointed out the loophole I was thinking about
Code coverage usually only tells you how many of the branches within a function are covered. It doesn't usually report the various paths that could be taken between function calls. Many errors in programs happen because the handoff from one method to another is wrong, not because the methods themselves contain errors. All bugs of this form could still exist in 100% code coverage.
In a recent IEEE Software paper "Two Mistakes and Error-Free Software: A Confession", Robert Glass argued that in the "real world" there are more bugs caused by what he calls missing logic or combinatorics (which can't be guarded against with code coverage tools) than by logic errors (which can).
In other words, even with 100% code coverage you still run the risk of encountering these kinds of errors. And the best thing you can do is--you guessed it--do more code reviews.
The reference to the paper is here and I found a rough summary here.
works on my machine
Many things work well on local machine and we cannot assure that to work on Staging/Production. Code Coverage may not cover this.
Errors in tests :)
Well if your tests don't test the thing which happens in the code covered.
If you have this method which adds a number to properties for example:
public void AddTo(int i)
{
NumberA += i;
NumberB -= i;
}
If your test only checks the NumberA property, but not NumberB, then you will have 100% coverage, the test passes, but NumberB will still contain an error.
Conclusion: a unit test with 100% will not guarantee that the code is bug-free.
Argument validation, aka. Null Checks. If you take any external inputs and pass them into functions but never check if they are valid/null, then you can achieve 100% coverage, but you will still get a NullReferenceException if you somehow pass null into the function because that's what your database gives you.
also, arithmetic overflow, like
int result = int.MAXVALUE + int.MAXVALUE;
Code Coverage only covers existing code, it will not be able to point out where you should add more code.
I don't know about anyone else, but we don't get anywhere near 100% coverage. None of our "This should never happen" CATCHes get exercised in our tests (well, sometimes they do, but then the code gets fixed so they don't any more!). I'm afraid I don't worry that there might be a Syntax/Logic error in a never-happen-CATCH
Your product might be technically correct, but not fulfil the needs of the customer.
FYI, Microsoft Pex attempts to help out by exploring your code and finding "edge" cases, like divide by zero, overflow, etc.
This tool is part of VS2010, though you can install a tech preview version in VS2008. It's pretty remarkable that the tool finds the stuff it finds, though, IME, it's still not going to get you all the way to "bulletproof".
Code Coverage doesn't mean much. What matters is whether all (or most) of the argument values that affect the behavior are covered.
For eg consider a typical compareTo method (in java, but applies in most languages):
//Return Negative, 0 or positive depending on x is <, = or > y
int compareTo(int x, int y) {
return x-y;
}
As long as you have a test for compareTo(0,0), you get code coverage. However, you need at least 3 testcases here (for the 3 outcomes). Still it is not bug free. It also pays to add tests to cover exceptional/error conditions. In the above case, If you try compareTo(10, Integer.MAX_INT), it is going to fail.
Bottomline: Try to partition your input to disjoint sets based on behavior, have a test for at least one input from each set. This will add more coverage in true sense.
Also check for tools like QuickCheck (If available for your language).
Perform a 100% code coverage, i.e., 100% instructions, 100% input and output domains, 100% paths, 100% whatever you think of, and you still may have bugs in your code: missing features.
As mentioned in many of the answers here, you could have 100% code coverage and still have bugs.
On top of that, you can have 0 bugs but the logic in your code may be incorrect (not matching the requirements). Code coverage, or being 100% bug-free can't help you with that at all.
A typical corporate software development practice could be as follows:
Have a clearly written functional specification
Have a test plan that's written against (1) and have it peer reviewed
Have test cases written against (2) and have them peer reviewed
Write code against the functional specification and have it peer reviewed
Test your code against the test cases
Do code coverage analysis and write more test cases to achieve good coverage.
Note that I said "good" and not "100%". 100% coverage may not always be feasible to achieve -- in which case your energy is best spent on achieving correctness of code, rather than the coverage of some obscure branches. Different sorts of things can go wrong in any of the steps 1 through 5 above: wrong idea, wrong specification, wrong tests, wrong code, wrong test execution... The bottom line is, step 6 alone is not the most important step in the process.
Concrete example of wrong code that doesn't have any bugs and has 100% coverage:
/**
* Returns the duration in milliseconds
*/
int getDuration() {
return end - start;
}
// test:
start = 0;
end = 1;
assertEquals(1, getDuration()); // yay!
// but the requirement was:
// The interface should have a method for returning the duration in *seconds*.
Almost anything.
Have you read Code Complete? (Because StackOverflow says you really should.) In Chapter 22 it says "100% statement coverage is a good start, but it is hardly sufficient". The rest of the chapter explains how to determine which additional tests to add. Here's a brief taster.
Structured basis testing and data flow testing means testing each logic path through the program. There are four paths through the contrived code below, depending on the values of A and B. 100% statement coverage could be achieved by testing only two of the four paths, perhaps f=1:g=1/f and f=0:g=f+1. But f=0:g=1/f will give a divide by zero error. You have to consider if statements and while and for loops (the loop body might never be executed) and every branch of a select or switch statement.
If A Then
f = 1
Else
f = 0
End If
If B Then
g = f + 1
Else
g = f / 0
End If
Error guessing - informed guesses about types of input that often cause errors. For instance boundary conditions (off by one errors), invalid data, very large values, very small values, zeros, nulls, empty collections.
And even so there can be errors in your requirements, errors in your tests, etc - as others have mentioned.