Test case design

Except for trivial Software Under TestSUTs, testing all possible casesexhaustive testing 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 Java: ArrayList,
Python: listcollection:

• 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}

A positive test case is when the test is designed to produce an expected/valid behavior. A negative test case is designed to produce a behavior that indicates an invalid/unexpected situation, such as an error message.

Consider testing of the method print(Integer i) which prints the value of i.

• A positive test case: i == new Integer(50)
• A negative test case: i == null;

Test case design can be of three types, based on how much of SUT 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.

_{Note: these videos are from the Udacity course Software Development Process by Georgia Tech}

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 failed 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.

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)

isValidMonth

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

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 value of s (GameStatus is an enum). In this case, each possible value for 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 method EPs given above for the 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]

When deciding EPs of OOP methods, you need to identify 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, 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 waiting for player’s first move
• IN_PLAY : the current minefield is already in use
• WON, LOST : let us assume the 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 (BVA) is 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 the boundary are often used in branching instructions etc. where the programmer can make mistakes.

markCellAt(int x, int y) operation could contain code such as if (x > 0 && x <= (W-1)) which involves 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.

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 boundary values
[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]	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: one element, two elements, no empty spaces, only one empty space

An SUT can take multiple inputs. You can select values for each input (using equivalence partitioning, boundary value analysis, or some other technique).

an SUT that takes multiple inputs and some values chosen as values for each input:

• Method to test: calculateGrade(participation, projectGrade, isAbsent, examScore)
• Values to test:

  Input	valid values to test	invalid values to test
  participation	0, 1, 19, 20	21, 22
  projectGrade	A, B, C, D, F
  isAbsent	true, false
  examScore	0, 1, 69, 70,	71, 72

Testing all possible combinations is effective but not efficient. If you test all possible combinations for the above example, you need to test 6x5x2x6=360 cases. Doing so has a higher chance of discovering bugs (i.e. effective) but the number of test cases can be too high (i.e. not efficient). Therefore, you need smarter ways to combine test inputs that are both effective and efficient.

Given below are some basic strategies for generating a set of test cases by combining multiple test input combination strategies.

Let's assume the SUT has the following three inputs and you have selected the given values for testing:

SUT: foo(p1 char, p2 int, p3 boolean)

Values to test:

Input	Values
p1	a, b, c
p2	1, 2, 3
p3	T, F

The all combinations strategy generates test cases for each unique combination of test inputs.

the strategy generates 3x3x2=18 test cases

Test Case	p1	p2	p3
1	a	1	T
2	a	1	F
3	a	2	T
...	...	...	...
18	c	3	F

The at least once strategy includes each test input at least once.

this strategy generates 3 test cases.

Test Case	p1	p2	p3
1	a	1	T
2	b	2	F
3	c	3	VV/IV

VV/IV = Any Valid Value / Any Invalid Value

The all pairs strategy creates test cases so that for any given pair of inputs, all combinations between them are tested. It is based on the observations that a bug is rarely the result of more than two interacting factors. The resulting number of test cases is lower than the all combinations strategy, but higher than the at least once approach.

this strategy generates 9 test cases:

Let's first consider inputs p1 and p2:

Input	Values
p1	a, b, c
p2	1, 2, 3

These values can generate (a,1)(a,2)(a,3)(b,1)(b,2),...3x3=9 combinations, and the test cases should cover all of them.

Next, let's consider p1 and p3.

Input	Values
p1	a, b, c
p3	T, F

These values can generate (a,T)(a,F)(b,T)(b,F),...3x2=6 combinations, and the test cases should cover all of them.

Similarly, inputs p2 and p3 generates another 6 combinations.

The 9 test cases given below covers all those 9+6+6 combinations.

Test Case	p1	p2	p3
1	a	1	T
2	a	2	T
3	a	3	F
4	b	1	F
5	b	2	T
6	b	3	F
7	c	1	T
8	c	2	F
9	c	3	T

A variation of this strategy is to test all pairs of inputs but only for inputs that could influence each other.

Testing all pairs between p1 and p3 only while ensuring all p2 values are tested at least once:

Test Case	p1	p2	p3
1	a	1	T
2	a	2	F
3	b	3	T
4	b	VV/IV	F
5	c	VV/IV	T
6	c	VV/IV	F

The random strategy generates test cases using one of the other strategies and then pick a subset randomly (presumably because the original set of test cases is too big).

There are other strategies that can be used too.

Use cases can be used for system testing and acceptance testing. For example, the main success scenario can be one test case while each variation (due to extensions) can form another test case. However, note that use cases do not specify the exact data entered into the system. Instead, it might say something like user enters his personal data into the system. Therefore, the tester has to choose data by considering equivalence partitions and boundary values. The combinations of these could result in one use case producing many test cases.

To increase E&E of testing, high-priority use cases are given more attention. For example, a scripted approach can be used to test high priority test cases, while an exploratory approach is used to test other areas of concern that could emerge during testing.

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 Efficient and EffectiveE&E, 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 E&E of testing.

Quality Assurance → Testing → Exploratory and Scripted Testing →

What

Here are two alternative approaches to testing a software: Scripted testing and Exploratory testing

1. Scripted testing: First write a set of test cases based on the expected behavior of the SUT, and then perform testing based on that set of test cases.

2. Exploratory testing: Devise test cases on-the-fly, creating new test cases based on the results of the past test cases.

Exploratory testing is ‘the simultaneous learning, test design, and test execution’ [source: bach-et-explained] whereby the nature of the follow-up test case is decided based on the behavior of the previous test cases. In other words, running the system and trying out various operations. It is called exploratory testing because testing is driven by observations during testing. Exploratory testing usually starts with areas identified as error-prone, based on the tester’s past experience with similar systems. One tends to conduct more tests for those operations where more faults are found.

Here is an example thought process behind a segment of an exploratory testing session:

“Hmm... looks like feature x is broken. This usually means feature n and k could be broken too; you need to look at them soon. But before that, you should give a good test run to feature y because users can still use the product if feature y works, even if x doesn’t work. Now, if feature y doesn’t work 100%, you have a major problem and this has to be made known to the development team sooner rather than later...”

Exploratory testing is also known as reactive testing, error guessing technique, attack-based testing, and bug hunting.

Exploratory Testing Explained, an online article by James Bach -- James Bach is an industry thought leader in software testing).

Scripted testing requires tests to be written in a scripting language; Manual testing is called exploratory testing.

• True
• False

False

Explanation: “Scripted” means test cases are predetermined. They need not be an executable script. However, exploratory testing is usually manual.

Which testing technique is better?

• a. error guessing
• b. bug hunting
• c. attack-based testing
• d. reactive testing
• e. These are different names used to describe exploratory testing.

(e)

Explain the concept of exploratory testing using Minesweeper as an example.

When we test the Minesweeper by simply playing it in various ways, especially trying out those that are likely to be buggy, that would be exploratory testing.