Testing

In OOP code, it is common to write one or more unit tests for each public method of a class.

class Foo {
    String read() {
        // ...
    }
    
    void write(String input) {
        // ...
    }
    
}

    

    


    
    

class FooTest {
    
    @Test
    void read() {
        // a unit test for Foo#read() method
    }
    
    @Test
    void write_emptyInput_exceptionThrown() {
        // a unit tests for Foo#write(String) method
    }  
    
    @Test
    void write_normalInput_writtenCorrectly() {
        // another unit tests for Foo#write(String) method
    }
}

    

    


    
    

import unittest

class Foo:
  def read(self):
      # ...
  
  def write(self, input):
      # ...


class FooTest(unittest.TestCase):
  
  def test_read(self):
      # a unit test for read() method
  
  def test_write_emptyIntput_ignored(self):
      # a unit test for write(string) method
  
  def test_write_normalInput_writtenCorrectly(self):
      # another unit test for write(string) method

    

    


    
    

A proper unit test requires the unit to be tested in isolation so that bugs in the cannot influence the test i.e. bugs outside of the unit should not affect the unit tests.

Stubs can isolate the from its dependencies.

class Logic {
    Storage s;

    Logic(Storage s) {
        this.s = s;
    }

    String getName(int index) {
        return "Name: " + s.getName(index);
    }
}

    

    


    
    

interface Storage {
    String getName(int index);
}

    

    


    
    

class DatabaseStorage implements Storage {

    @Override
    public String getName(int index) {
        return readValueFromDatabase(index);
    }

    private String readValueFromDatabase(int index) {
        // retrieve name from the database
    }
}

    

    


    
    

Logic logic = new Logic(new DatabaseStorage());
String name = logic.getName(23);

    

    


    
    

@Test
void getName() {
    Logic logic = new Logic(new DatabaseStorage());
    assertEquals("Name: John", logic.getName(5));
}

    

    


    
    

class StorageStub implements Storage {

    @Override
    public String getName(int index) {
        if (index == 5) {
            return "Adam";
        } else {
            throw new UnsupportedOperationException();
        }
    }
}

    

    


    
    

@Test
void getName() {
    Logic logic = new Logic(new StorageStub());
    assertEquals("Name: Adam", logic.getName(5));
}

    

    


    
    

In addition to Stubs, there are other type of replacements you can use during testing, e.g. Mocks, Fakes, Dummies, Spies.

Integration testing : testing whether different parts of the software work together (i.e. integrates) as expected. Integration tests aim to discover bugs in the 'glue code' related to how components interact with each other. These bugs are often the result of misunderstanding what the parts are supposed to do vs what the parts are actually doing.

Integration testing is not simply a case of repeating the unit test cases using the actual dependencies (instead of the stubs used in unit testing). Instead, integration tests are additional test cases that focus on the interactions between the parts.

In practice, developers often use a hybrid of unit+integration tests to minimize the need for stubs.

System testing is typically done by a testing team (also called a QA team).

System test cases are based on the specified external behavior of the system. Sometimes, system tests go beyond the bounds defined in the specification. This is useful when testing that the system fails 'gracefully' when pushed beyond its limits.

Test case: load a web page that is too big
* Input: loads a web page containing more than 5000 characters.
* Expected behavior: aborts the loading of the page
  and shows a meaningful error message.

    

    


    
    

System testing includes testing against non-functional requirements too. Here are some examples:

• Performance testing – to ensure the system responds quickly.
• Load testing (also called stress testing or scalability testing) – to ensure the system can work under heavy load.
• Security testing – to test how secure the system is.
• Compatibility testing, interoperability testing – to check whether the system can work with other systems.
• Usability testing – to test how easy it is to use the system.
• Portability testing – to test whether the system works on different platforms.

Alpha testing is performed by the users, under controlled conditions set by the software development team.

Beta testing is performed by a selected subset of target users of the system in their natural work setting.

An open beta release is the release of not-yet-production-quality-but-almost-there software to the general population. For example, Google’s Gmail was in 'beta' for many years before the label was finally removed.

Eating your own dog food (aka dogfooding), is when creators use their own product so as to test the product.

Developer testing is the testing done by the developers themselves as opposed to professional testers or end-users.

Delaying testing until the full product is complete has a number of disadvantages:

• Locating the cause of a test case failure is difficult due to a large search space; in a large system, the search space could be millions of lines of code, written by hundreds of developers! The failure may also be due to multiple inter-related bugs.
• Fixing a bug found during such testing could result in major rework, especially if the bug originated from the design or during requirements specification i.e. a faulty design or faulty requirements.
• One bug might 'hide' other bugs, which could emerge only after the first bug is fixed.
• The delivery may have to be delayed if too many bugs are found during testing.

Therefore, it is better to do early testing, as hinted by the popular rule of thumb given below, also illustrated by the graph below it.

The earlier a bug is found, the easier and cheaper to have it fixed.

Such early testing of partially developed software is usually, and by necessity, done by the developers themselves i.e. developer testing.

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.

Which approach is better – scripted or exploratory? A mix is better.

The success of exploratory testing depends on the tester’s prior experience and intuition. Exploratory testing should be done by experienced testers, using a clear strategy/plan/framework. Ad-hoc exploratory testing by unskilled or inexperienced testers without a clear strategy is not recommended for real-world non-trivial systems. While exploratory testing may allow us to detect some problems in a relatively short time, it is not prudent to use exploratory testing as the sole means of testing a critical system.

Scripted testing is more systematic, and hence, likely to discover more bugs given sufficient time, while exploratory testing would aid in quick error discovery, especially if the tester has a lot of experience in testing similar systems.

In some contexts, you will achieve your testing mission better through a more scripted approach; in other contexts, your mission will benefit more from the ability to create and improve tests as you execute them. I find that most situations benefit from a mix of scripted and exploratory approaches. --[source: bach-et-explained]

Acceptance tests give an assurance to the customer that the system does what it is intended to do. Acceptance test cases are often defined at the beginning of the project, usually based on the use case specification. Successful completion of UAT is often a prerequisite to the project sign-off.

Acceptance testing comes after system testing. Similar to system testing, acceptance testing involves testing the whole system.

Some differences between system testing and acceptance testing:

Note: negative test cases: cases where the SUT is not expected to work normally e.g. incorrect inputs; positive test cases: cases where the SUT is expected to work normally

Passing system tests does not necessarily mean passing acceptance testing. Some examples:

• The system might work on the testbed environments but might not work the same way in the deployment environment, due to subtle differences between the two environments.
• The system might conform to the system specification but could fail to solve the problem it was supposed to solve for the user, due to flaws in the system design.

When you modify a system, the modification may result in some unintended and undesirable effects on the system. Such an effect is called a regression.

Regression testing is the re-testing of the software to detect regressions. Note that to detect regressions, you need to retest all related components, even if they had been tested before.

Regression testing is more effective when it is done frequently, after each small change. However, doing so can be prohibitively expensive if testing is done manually. Hence, regression testing is more practical when it is automated.