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Test case design

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Introduction

What

Except for trivial , is not practical because such testing often requires a massive/infinite number of test cases.

Consider the test cases for adding a string object to a :

  • Add an item to an empty collection.
  • Add an item when there is one item in the collection.
  • Add an item when there are 2, 3, .... n items in the collection.
  • Add an item that has an English, a French, a Spanish, ... word.
  • Add an item that is the same as an existing item.
  • Add an item immediately after adding another item.
  • Add an item immediately after system startup.
  • ...

Exhaustive testing of this operation can take many more test cases.

Program testing can be used to show the presence of bugs, but never to show their absence!
--Edsger Dijkstra

Every test case adds to the cost of testing. In some systems, a single test case can cost thousands of dollars e.g. on-field testing of flight-control software. Therefore, test cases need to be designed to make the best use of testing resources. In particular:

  • Testing should be effective i.e., it finds a high percentage of existing bugs e.g., a set of test cases that finds 60 defects is more effective than a set that finds only 30 defects in the same system.

  • Testing should be efficient i.e., it has a high rate of success (bugs found/test cases) a set of 20 test cases that finds 8 defects is more efficient than another set of 40 test cases that finds the same 8 defects.

For testing to be , each new test you add should be targeting a potential fault that is not already targeted by existing test cases. There are test case design techniques that can help us improve the E&E of testing.

Black Box vs Glass Box

Test case design can be of three types, based on how much of the SUT's internal details are considered when designing test cases:

  • Black-box (aka specification-based or responsibility-based) approach: test cases are designed exclusively based on the SUT’s specified external behavior.

  • White-box (aka glass-box or structured or implementation-based) approach: test cases are designed based on what is known about the SUT’s implementation, i.e. the code.

  • Gray-box approach: test case design uses some important information about the implementation. For example, if the implementation of a sort operation uses different algorithms to sort lists shorter than 1000 items and lists longer than 1000 items, more meaningful test cases can then be added to verify the correctness of both algorithms.

Black-box and white-box testing

Equivalence partitions

What

Consider the testing of the following operation.

isValidMonth(m) : returns true if m (and int) is in the range [1..12]

It is inefficient and impractical to test this method for all integer values [-MIN_INT to MAX_INT]. Fortunately, there is no need to test all possible input values. For example, if the input value 233 fails to produce the correct result, the input 234 is likely to fail too; there is no need to test both.

In general, most SUTs do not treat each input in a unique way. Instead, they process all possible inputs in a small number of distinct ways. That means a range of inputs is treated the same way inside the SUT. Equivalence partitioning (EP) is a test case design technique that uses the above observation to improve the E&E of testing.

Equivalence partition (aka equivalence class): A group of test inputs that are likely to be processed by the SUT in the same way.

By dividing possible inputs into equivalence partitions you can,

  • avoid testing too many inputs from one partition. Testing too many inputs from the same partition is unlikely to find new bugs. This increases the efficiency of testing by reducing redundant test cases.
  • ensure all partitions are tested. Missing partitions can result in bugs going unnoticed. This increases the effectiveness of testing by increasing the chance of finding bugs.

Basic

Equivalence partitions (EPs) are usually derived from the specifications of the SUT.

These could be EPs for the isValidMonth example:

  • [MIN_INT ... 0]: below the range that produces true (produces false)
  • [1 … 12]: the range that produces true
  • [13 … MAX_INT]: above the range that produces true (produces false)

When the SUT has multiple inputs, you should identify EPs for each input.

Consider the method duplicate(String s, int n): String which returns a String that contains s repeated n times.

Example EPs for s:

  • zero-length strings
  • string containing whitespaces
  • ...

Example EPs for n:

  • 0
  • negative values
  • ...

An EP may not have adjacent values.

Consider the method isPrime(int i): boolean that returns true if i is a prime number.

EPs for i:

  • prime numbers
  • non-prime numbers

Some inputs have only a small number of possible values and a potentially unique behavior for each value. In those cases, you have to consider each value as a partition by itself.

Consider the method showStatusMessage(GameStatus s): String that returns a unique String for each of the possible values of s (GameStatus is an enum). In this case, each possible value of s will have to be considered as a partition.

Note that the EP technique is merely a heuristic and not an exact science, especially when applied manually (as opposed to using an automated program analysis tool to derive EPs). The partitions derived depend on how one ‘speculates’ the SUT to behave internally. Applying EP under a glass-box or gray-box approach can yield more precise partitions.

Consider the EPs given above for the method isValidMonth. A different tester might use these EPs instead:

  • [1 … 12]: the range that produces true
  • [all other integers]: the range that produces false

Some more examples:

Specification Equivalence partitions

isValidFlag(String s): boolean
Returns true if s is one of ["F", "T", "D"]. The comparison is case-sensitive.

["F"] ["T"] ["D"] ["f", "t", "d"] [any other string][null]

squareRoot(String s): int
Pre-conditions: s represents a positive integer.
Returns the square root of s if the square root is an integer; returns 0 otherwise.

[s is not a valid number] [s is a negative integer] [s has an integer square root] [s does not have an integer square root]

Intermediate

When deciding EPs of OOP methods, you need to identify the EPs of all data participants that can potentially influence the behaviour of the method, such as,

  • the target object of the method call
  • input parameters of the method call
  • other data/objects accessed by the method such as global variables. This category may not be applicable if using the black box approach (because the test case designer using the black box approach will not know how the method is implemented).

Consider this method in the DataStack class: push(Object o): boolean

  • Adds o to the top of the stack if the stack is not full.
  • Returns true if the push operation was a success.
  • Throws
    • MutabilityException if the global flag FREEZE==true.
    • InvalidValueException if o is null.

EPs:

  • DataStack object: [full] [not full]
  • o: [null] [not null]
  • FREEZE: [true][false]

Consider a simple Minesweeper app. What are the EPs for the newGame() method of the Logic component?

As newGame() does not have any parameters, the only obvious participant is the Logic object itself.

Note that if the glass-box or the grey-box approach is used, other associated objects that are involved in the method might also be included as participants. For example, the Minefield object can be considered as another participant of the newGame() method. Here, the black-box approach is assumed.

Next, let us identify equivalence partitions for each participant. Will the newGame() method behave differently for different Logic objects? If yes, how will it differ? In this case, yes, it might behave differently based on the game state. Therefore, the equivalence partitions are:

  • PRE_GAME: before the game starts, minefield does not exist yet
  • READY: a new minefield has been created and the app is waiting for the player’s first move
  • IN_PLAY: the current minefield is already in use
  • WON, LOST: let us assume that newGame() behaves the same way for these two values

Consider the Logic component of the Minesweeper application. What are the EPs for the markCellAt(int x, int y) method? The partitions in bold represent valid inputs.

  • Logic: PRE_GAME, READY, IN_PLAY, WON, LOST
  • x: [MIN_INT..-1] [0..(W-1)] [W..MAX_INT] (assuming a minefield size of WxH)
  • y: [MIN_INT..-1] [0..(H-1)] [H..MAX_INT]
  • Cell at (x,y): HIDDEN, MARKED, CLEARED

Boundary value analysis

What

Boundary Value Analysis (BVA) is a test case design heuristic that is based on the observation that bugs often result from incorrect handling of boundaries of equivalence partitions. This is not surprising, as the end points of boundaries are often used in branching instructions, etc., where the programmer can make mistakes.

The markCellAt(int x, int y) operation could contain code such as if (x > 0 && x <= (W-1)) which involves the boundaries of x’s equivalence partitions.

BVA suggests that when picking test inputs from an equivalence partition, values near boundaries (i.e. boundary values) are more likely to find bugs.

Boundary values are sometimes called corner cases.

How

Typically, you should choose three values around the boundary to test: one value from the boundary, one value just below the boundary, and one value just above the boundary. The number of values to pick depends on other factors, such as the cost of each test case.

Some examples:

Equivalence partition Some possible test values (boundaries are in bold)

[1-12]

0,1,2, 11,12,13

[MIN_INT, 0]
(MIN_INT is the minimum possible integer value allowed by the environment)

MIN_INT, MIN_INT+1, -1, 0 , 1

[any non-null String]
(assuming string length is the aspect of interest)

Empty String, a String of maximum possible length

[prime numbers]
[“F”]
[“A”, “D”, “X”]

No specific boundary
No specific boundary
No specific boundary

[non-empty Stack]
(assuming a fixed size stack)

Stack with: no elements, one element, two elements, no empty spaces, only one empty space