Test Coverage and Techniques

Search…
Time is a crucial constraint in software development and software teams often need to focus their test efforts on the most important application paths.
⚠ UNDER CONSTRUCTION ⚠
In software testing, test coverage is some kind of metric that helps you to understand what parts of your application (or code) are exercised by your tests. The number of feasible paths through them grows exponentially with an increase in application size and can even be infinite in the case of applications with unbounded loop iterations. That is a problem called path explosion problem. Concordia Compiler deals with it by providing sets of combination and selection strategies, and trying to achieve full path coverage over time.
Test Coverage
All the Features, Scenarios, and Variants are covered by default.
The CLI parameter --files can filter the .feature files to be considered.
The CLI parameter --ignore can indicate the .feature files to be ignored, when a directory is given.
The tag @ignore can be used to mark a Feature or Variant to be ignored by the test generator. However, it can still be used by other Features or Variants.
The tag @importance  (e.g., @importance( 8 )) can be used to denote the importance of a Feature. CLI parameters --sel-min-feature and --sel-max-feature can then be used to filter the features to be considered by the test generator. Example: concordia --sel-min-feature 7 makes the compiler considers the features with importance value of 7 or above.  By default, all the features receive an importance value of 5. 
Variants are selected and combined using State-based Strategies (see below).
All the UI Elements constraints are covered by default, using a set of Testing Techniques (see below).
State-based Strategies
In Concordia, you can declare a State in a Variant sentence using a text between tile (~), like this:
1
Given that I have ~payment method selected~
Copied!
There are three types of State:
1.Precondition: when declared in a Given sentence;
2.State Call: when declared in a When sentence;
3.Postcondition: when declared in a Then sentence.
Both Preconditions and State Calls are considered required States. That is, they denote a dependency of a certain State of the system that needs to be executed. A Precondition needs to be executed before the Variant starts, and a State Call needs to be executed during the Variant's execution.
A Postcondition is a produced State, that is, a state of the system produced by a successful execution of a Variant. Therefore, whether something goes wrong during a Variant's execution, it will not produce the declared State.
When the current Variant requires a State, Concordia Compiler will look for imported Features' Variants able to produce it. To generate complete Test Cases for the current Variant, it will:
1.Select the Variants to combine;
2.Generate successful test scenarios for the selected Variants;
3.Select the successful test scenarios to combine;
4.Generate (successful and unsuccessful) test scenarios for the current Variant;
5.Combine the selected successful test scenarios with the test scenarios of the current Variant;
6.Transform all the test scenarios into test cases (i.e., valued test scenarios).
Steps 1 and 3 can adopt different strategies. Concordia Compiler let's you:
Parameterize how the Variants will be selected, using --comb-variant; and 
Parameterize how the successful test scenarios will be combined, using --comb-state.
Variant selection
Available strategies for --comb-variant:
random: Selects a random Variant that produces the required State. That's the default behavior;
first: Selects the first Variant that produces the required State;
fmi: Selects the first most important Variant (since two Variants can have the same importance value) that produces the required State;
all: Selects all the Variants that produce the required State.
Example:
1
npx concordia --comb-variant=all
Copied!
State combination
Available strategies for --comb-state:
sre: Single random of each - that is, randomly selects a single, successful test scenario of each selected Variant. That's the default behavior;
sow : Shuffled one-wise - that is, shuffles the successful test scenarios than uses one-wise combination. 
ow: One-wise selection;
all: Selects all the successful test scenarios to combine.
Example:
1
npx concordia --comb-state=all
Copied!
Full vs random selection
Strategies that use random selection can take different paths every time they are used. Furthermore, they reduce considerably the amount of generated paths - i.e., it avoids "path explosion" - and thus the amount of produced test cases.
Full-selection strategies can be used for increase path coverage. Although, it also increases the needed time to check all the paths, which may be undesirable for frequent tests.
By default, Concordia Compiler uses random selection strategies.
Testing Techniques
Concordia Compiler can infer input test data from Variants, UI Elements, Constants, Tables, and Databases. The more constraints you declare, the more test cases it generates.
Adopted techniques to generate input test data include:
​Boundary-value analysis​
​Equivalence partitioning​
​Random testing​
​Special cases​
These are well-known, effective black-box testing techniques for discovering relevant defects on applications.
Data Test Cases
We call Data Test Cases those test cases used to generate input test data. They are classified into the following groups: RANGE, LENGTH, FORMAT, SET, REQUIRED, and COMPUTED. The group COMPUTEDis not available on purpose, since a user-defined algorithm to produce test data can have bugs on itself. Thus, one should provide expected input and output values in order to check whether the application is able to correctly compute the output value based on the received input value.
Every group has a set of related data test cases, applied according to the declared constraints and selected algorithms:
Group
Data Test Case
Description
RANGE
LOWEST_VALUE
The lowest value for the data type, e.g., lowest integer
​
RANDOM_BELOW_MIN_VALUE
A random value below the minimum value
​
JUST_BELOW_MIN_VALUE
The value just below the minimum value, considering the data type and decimal places if applicable
​
MIN_VALUE
Exactly the minimum value
​
JUST_ABOVE_MIN_VALUE
The value just above the minimum value, considering the data type and decimal places if applicable
​
ZERO_VALUE
Zero (0)
​
MEDIAN_VALUE
The median between the minimum value and the maximum value
​
RANDOM_BETWEEN_MIN_MAX_VALUES
A random value between the minimum value and the maximum value
​
JUST_BELOW_MAX_VALUE
The value just below the maximum value, considering the data type and decimal places if applicable
​
MAX_VALUE
Exactly the maximum value
​
JUST_ABOVE_MAX_VALUE
The value just above the maximum value, considering the data type and decimal places if applicable
​
RANDOM_ABOVE_MAX_VALUE
A random value above the maximum value
​
GREATEST_VALUE
The greatest value for the data type, e.g., greatest integer
LENGTH
LOWEST_LENGTH
An empty string
​
RANDOM_BELOW_MIN_LENGTH
A string with random characters and random length, less than the minimum length
​
JUST_BELOW_MIN_LENGTH
A string with random characters and length exactly below the minimum length
​
MIN_LENGTH
A string with random characters and length exactly equal to the minimum length
​
JUST_ABOVE_MIN_LENGTH
A string with random characters and length exactly above the minimum length
​
MEDIAN_LENGTH
A string with random characters and length equal to the median between the minimum length and the maximum length
​
Maximum length for random string values
By default, the maximum length for randomly-generated string values is 500. This value is used for reducing the time to run test scripts, since long strings take time to be entered.
You can set maximum length using the CLI parameter --random-max-string-size. Example: 
1
npx concordia --random-max-string-size=300
Copied!
You can also set it in the configuration file (.concordiarc) by adding the property  randomMaxStringSize. Example:
1
"randomMaxStringSize": 300
Copied!
​
Properties vs Data Test Cases
Data Test Cases (DTC) are selected for every declared UI Element and its properties. The more properties you declare for a UI Element, the more data you provide for Concordia Compiler to generate DTC.
Example of some evalutations:
When no properties are declared, FILLED and NOT_FILLED are both applied and considered as valid values;
When the property required is declared, NOT_FILLED (empty) is considered as an invalid value;
When the property value is declared:
if it comes from a set of values (inclusing a query result), all the DTC of the group SET are applied;
otherwise, ...
When the property minimum value is declared:
There is more logic involved for generating these values. ...
Example 1
Let's describe a user interface element named Salary :
1
UI Element: Salary
2
  - data type is double
Copied!
When no property is defined or only the property data typeis defined, 
We defined the property data type as double, since the default data type is string.   
Since few restrictions were made, Salary will be tested with the test cases of the group REQUIRED: 
1.FILLED: a pseudo-random double value is generated;
2.NOT_FILLED: an empty value will be used.
Now let's add a minimum value restriction.
1
UI Element: Salary
2
  - data type is double
3
  - minimum value is 1000.00
4
    Otherwise I must see "Salary must be greater than or equal to 1000"
Copied!
Some tests of the group RANGE are now applicable:
1.LOWEST_VALUE: the lowest possible double is used
2.RANDOM_BELOW_MIN_VALUE: a random double before the minimum value is generated
3.JUST_BELOW_MIN_VALUE: a double just below the minimum value is used (e.g., 999.99)
4.MIN_VALUE: the minimum value is used
5.JUST_ABOVE_MIN_VALUE: a double just above the minimum value is used (e.g., 1000.01)
6.ZERO_VALUE: zero (0) is used
Since 1000.00 is the minimum value, the data produced by the tests 1, 2, 3, and 6 of the group VALUE are considered invalid, while 4 and 5 are not. For these tests considered invalid, the behavior defined in Otherwise, that is
1
    Otherwise I must see "Salary must be greater than or equal to 1000"
Copied!
is expected to happen. In other words, this behavior serves as test oracle and must occur only when the produced value is invalid.
Unlike this example, when the expected system behavior for invalid values is not specified and a test data is considered invalid, Concordia expects that test should fail. In this case, it generates the Test Case with the tag @fail.
Now let's add maximum value restriction:
1
UI Element: Salary
2
  - data type is double
3
  - minimum value is 1000.00
4
    Otherwise I must see "Salary must be greater than or equal to 1000"
5
  - maximum value is 30000.00
6
    Otherwise I must see "Salary must be less than or equal to 30000"
Copied!
All the tests of the group RANGE are now applicable. That is, the following tests will be included:
1.MEDIAN_VALUE: the median between the minimum and the maximum values
2.RANDOM_BETWEEN_MIN_MAX_VALUES: a pseudo-random double value between the minimum and the maximum values
3.JUST_BELOW_MAX_VALUE: the value just below the maximum value
4.MAX_VALUE: the maximum value
5.JUST_ABOVE_MAX_VALUE: the value just above the maximum value
6.RANDOM_ABOVE_MAX_VALUE: a pseudo-random double above the maximum value
7.GREATEST_VALUE: the greatest possible double
The tests from 5 to 7 will produce values considered invalid.
Example 2
Let's define a user interface element named Profession and a table named Professions from which the values come from:
1
UI Element: Profession
2
  - type is select
3
  - value comes from "SELECT name from [Professions]"
4
  - required
5
​
6
Table: Professions
7
  | name       |
8
  | Lawyer     |
9
  | Accountant |
10
  | Dentist    |
11
  | Professor  |
12
  | Mechanic   |
Copied!
Applicable test are:
FILLED
NOT_FILLED
FIRST_ELEMENT
RANDOM_ELEMENT
LAST_ELEMENT
NOT_IN_SET
The first two tests are in the group REQUIRED. Since we declared Profession as having a required value, the test FILLED is considered valid but NOT_FILLED is not. Therefore, it is important to remember declaring required inputs accordingly.
The last four tests are in the group SET. Only the last one, NOT_IN_SET, will produce a value considered invalid.
Example 3
In this example, let's adjust the past two examples to make Salary rules dynamic and change according to the Profession.
Firstly, we add two columns the the Professions table:
1
Table: professions
2
  | name       | min_salary | max_salary |
3
  | Lawyer     | 100000     |  900000    |
4
  | Accountant |  90000     |  800000    |
5
  | Dentist    | 150000     |  900000    |
6
  | Professor  |  80000     |  500000    |
7
  | Mechanic   |  50000     |  120000    |
Copied!
Then, we change the rules to retrieve the values from the table:
1
UI Element: Salary
2
  - data type is double
3
  - minimum value comes from the query "SELECT min_salary FROM [Professions] WHERE name = {Profession}"
4
    Otherwise I must see "The given Salary is less than the minimum value"
5
  - maximum value comes from the query "SELECT max_salary FROM [Professions] WHERE name = {Profession}"
6
    Otherwise I must see "The given Salary is greater than the maximum value"
Copied!
The reference to the UI Element {Profession} inside the query, makes the rules of Salary depend on Profession. Every time a Profession is selected, the minimum value and the maximum value of Salary changes according to the columns min_salary and max_salary of the table Professions.