Most programs are written to solve complex problems involving large amounts of intricate details. It is impossible to deal with all these details at the same time. The guiding principle of abstraction stipulates that we capture only details that are relevant to the current perspective or the task at hand.

Ignoring lower level data items and thinking in terms of bigger entities is called data abstraction.

Control abstraction abstracts away details of the actual control flow to focus on tasks at a simplified level.

Abstraction can be applied repeatedly to obtain progressively higher levels of abstractions.

Coupling is a measure of the degree of dependence between components, classes, methods, etc. Low coupling indicates that a component is less dependent on other components. High coupling (aka tight coupling or strong coupling) is discouraged due to the following disadvantages:

• Maintenance is harder because a change in one module could cause changes in other modules coupled to it (i.e. a ripple effect).
• Integration is harder because multiple components coupled with each other have to be integrated at the same time.
• Testing and reuse of the module is harder due to its dependence on other modules.

In the example below, design A appears to have a more coupling between the components than design B.

X is coupled to Y if a change to Y can potentially require a change in X.

class Foo{
    ...
    new Bar().read();
    ...
}

class Bar{
    void read(){
        ...
    }
    
    void write(){
        ...
    }
}

Some examples of different coupling types:

• Content coupling: one module modifies or relies on the internal workings of another module  e.g., accessing local data of another module
• Common/Global coupling: two modules share the same global data
• Control coupling: one module controlling the flow of another, by passing it information on what to do  e.g., passing a flag
• Data coupling: one module sharing data with another module  e.g. via passing parameters
• External coupling: two modules share an externally imposed convention  e.g., data formats, communication protocols, device interfaces.
• Subclass coupling: a class inherits from another class. Note that a child class is coupled to the parent class but not the other way around.
• Temporal coupling: two actions are bundled together just because they happen to occur at the same time  e.g. extracting a contiguous block of code as a method although the code block contains statements unrelated to each other

Cohesion is a measure of how strongly-related and focused the various responsibilities of a component are. A highly-cohesive component keeps related functionalities together while keeping out all other unrelated things.

Higher cohesion is better. Disadvantages of low cohesion (aka weak cohesion):

• Impedes the understandability of modules as it is difficult to express module functionalities at a higher level.
• Lowers maintainability because a module can be modified due to unrelated causes  (reason: the module contains code unrelated to each other) or many many modules may need to be modified to achieve a small change in behavior  (reason: because the code realated to that change is not localized to a single module).
• Lowers reusability of modules because they do not represent logical units of functionality.

Cohesion can be present in many forms. Some examples:

• Code related to a single concept is kept together, e.g. the Student component handles everything related to students.
• Code that is invoked close together in time is kept together, e.g. all code related to initializing the system is kept together.
• Code that manipulates the same data structure is kept together, e.g. the GameArchive component handles everything related to the storage and retrieval of game sessions.

A concern in this context is a set of information that affects the code of a computer program.

Applying SoC reduces functional overlaps among code sections and also limits the ripple effect when changes are introduced to a specific part of the system.

This principle can be applied at the class level, as well as on higher levels.

Design → Architecture → Styles → n-Tier Style

What

In the n-tier style, higher layers make use of services provided by lower layers. Lower layers are independent of higher layers. Other names: multi-layered, layered.

Operating systems and network communication software often use n-tier style.

This principle should lead to higher cohesion and lower coupling.

Design → Design Fundamentals → Coupling →

What

Coupling is a measure of the degree of dependence between components, classes, methods, etc. Low coupling indicates that a component is less dependent on other components. High coupling (aka tight coupling or strong coupling) is discouraged due to the following disadvantages:

• Maintenance is harder because a change in one module could cause changes in other modules coupled to it (i.e. a ripple effect).
• Integration is harder because multiple components coupled with each other have to be integrated at the same time.
• Testing and reuse of the module is harder due to its dependence on other modules.

In the example below, design A appears to have a more coupling between the components than design B.

Discuss the coupling levels of alternative designs x and y.

Overall coupling levels in x and y seem to be similar (neither has more dependencies than the other). (Note that the number of dependency links is not a definitive measure of the level of coupling. Some links may be stronger than the others.). However, in x, A is highly-coupled to the rest of the system while B, C, D, and E are standalone (do not depend on anything else). In y, no component is as highly-coupled as A of x. However, only D and E are standalone.

Explain the link (if any) between regressions and coupling.

When the system is highly-coupled, the risk of regressions is higher too  e.g. when component A is modified, all components ‘coupled’ to component A risk ‘unintended behavioral changes’.

Discuss the relationship between coupling and testability.

Coupling decreases testability because if the SUT is coupled to many other components it becomes difficult to test the SUI in isolation of its dependencies.

Choose the correct statements.

• a. As coupling increases, testability decreases.
• b. As coupling increases, the risk of regression increases.
• c. As coupling increases, the value of automated regression testing increases.
• d. As coupling increases, integration becomes easier as everything is connected together.
• e. As coupling increases, maintainability decreases.

(a)(b)(c)(d)(e)

Explanation: High coupling means either more components require to be integrated at once in a big-bang fashion (increasing the risk of things going wrong) or more drivers and stubs are required when integrating incrementally.

While it is possible to isolate the functionalities of a software system into modules, there is no way to remove interaction between modules. When modules interact with each other, coupling naturally increases. Consequently, it is harder to localize any changes to the software system. The Open-Close Principle aims to alleviate this problem.

In object-oriented programming, OCP can be achieved in various ways. This often requires separating the specification (i.e. interface) of a module from its implementation.

In the design given below, the behavior of the CommandQueue class can be altered by adding more concrete Command subclasses. For example, by including a Delete class alongside List, Sort, and Reset, the CommandQueue can now perform delete commands without modifying its code at all. That is, its behavior was extended without having to modify its code. Hence, it was open to extensions, but closed to modification.

ArrayList students = new ArrayList< Student >();
ArrayList admins = new ArrayList< Admin >();  	

In terms of timing and frequency, there are two general approaches to integration: late and one-time, early and frequent.

Late and one-time: wait till all components are completed and integrate all finished components near the end of the project.

Early and frequent: integrate early and evolve each part in parallel, in small steps, re-integrating frequently.

Big-bang integration: integrate all components at the same time.

Incremental integration: integrate few components at a time. This approach is better than the big-bang integration because it surfaces integration problems in a more manageable way.

Based on the order in which components are integrated, incremental integration can be done in three ways.

Top-down integration: higher-level components are integrated before bringing in the lower-level components. One advantage of this approach is that higher-level problems can be discovered early. One disadvantage is that this requires the use of stubs in place of lower level components until the real lower-level components are integrated to the system. Otherwise, higher-level components cannot function as they depend on lower level ones.

Bottom-up integration: the reverse of top-down integration. Note that when integrating lower level components, drivers may be needed to test the integrated components  because the UI may not be integrated yet, just like top-down integration needs stubs.

Sandwich integration: a mix of the top-down and the bottom-up approaches. The idea is to do both top-down and bottom-up so as to 'meet' in the middle.

Unit testing : testing individual units (methods, classes, subsystems, ...) to ensure each piece works correctly.

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(sefl):
      # a unit test for read() method
  
  def test_write_emptyIntput_ignored(self):
      # a unit tests for write(string) method
  
  def test_write_normalInput_writtenCorrectly(self):
      # another unit tests for write(string) method

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

Stubs can isolate the SUT 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.

Dependency injection is the process of 'injecting' objects to replace current dependencies with a different object. This is often used to inject stubs to isolate the SUT from its dependencies so that it can be tested in isolation.

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));
}

A Foo object normally depends on a Bar object, but we can inject a BarStub object so that the Foo object no longer depends on a Bar object. Now we can test the Foo object in isolation from the Bar object.

Polymorphism can be used to implement dependency injection, as can be seen in the example given in [Quality Assurance → Testing → Unit Testing → Stubs] where a stub is injected to replace a dependency.

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));
}

class Payroll {
    private SalaryManager manager = new SalaryManager();
    private String[] employees;

    void setEmployees(String[] employees) {
        this.employees = employees;
    }

    void setSalaryManager(SalaryManager sm) {
       this. manager = sm;
    }

    double totalSalary() {
        double total = 0;
        for(int i = 0;i < employees.length; i++){
            total += manager.getSalaryForEmployee(employees[i]);
        }
        return total;
    }
}


class SalaryManager {
    double getSalaryForEmployee(String empID){
        //code to access employee’s salary history
        //code to calculate total salary paid and return it
    }
}

class PayrollTest {
    public static void main(String[] args) {
        //test setup
        Payroll p = new Payroll();
        p.setSalaryManager(new SalaryManagerStub()); //dependency injection
        //test case 1
        p.setEmployees(new String[]{"E001", "E002"});
        assertEquals(2500.0, p.totalSalary());
        //test case 2
        p.setEmployees(new String[]{"E001"});
        assertEquals(1000.0, p.totalSalary());
        //more tests ...
    }
}


class SalaryManagerStub extends SalaryManager {
    /** Returns hard coded values used for testing */
    double getSalaryForEmployee(String empID) {
        if(empID.equals("E001")) {
            return 1000.0;
        } else if(empID.equals("E002")) {
            return 1500.0;
        } else {
            throw new Error("unknown id");
        }
    }
}

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 of what the parts are supposed to do vs what the parts are actually doing.

Integration testing is not simply a repetition of the unit test cases but run 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: take the whole system and test it against the system specification.

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' having pushed beyond its limits.

Test case: load a web page that is too big
* Input: load a web page containing more than 5000 characters. 
* Expected behavior: abort the loading of the page and show 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.

If a software product has a GUI component, all product-level testing (i.e. the types of testing mentioned above) need to be done using the GUI. However, testing the GUI is much harder than testing the CLI (command line interface) or API, for the following reasons:

• Most GUIs can support a large number of different operations, many of which can be performed in any arbitrary order.
• GUI operations are more difficult to automate than API testing. Reliably automating GUI operations and automatically verifying whether the GUI behaves as expected is harder than calling an operation and comparing its return value with an expected value. Therefore, automated regression testing of GUIs is rather difficult.
• The appearance of a GUI (and sometimes even behavior) can be different across platforms and even environments. For example, a GUI can behave differently based on whether it is minimized or maximized, in focus or out of focus, and in a high resolution display or a low resolution display.

One approach to overcome the challenges of testing GUIs is to minimize logic aspects in the GUI. Then, bypass the GUI to test the rest of the system using automated API testing. While this still requires the GUI to be tested manually, the number of such manual test cases can be reduced as most of the system has been tested using automated API testing.

There are testing tools that can automate GUI testing.

Acceptance testing (aka User Acceptance Testing (UAT)): test the delivered system to ensure it meets the user requirements.

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.

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.

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 such 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 during 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 were 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.

A test driver is the code that ‘drives’ the SUT for the purpose of testing i.e. invoking the SUT with test inputs and verifying the behavior is as expected.

public class PayrollTestDriver {
    public static void main(String[] args) throws Exception {

        //test setup
        Payroll p = new Payroll();

        //test case 1
        p.setEmployees(new String[]{"E001", "E002"});
        // automatically verify the response
        if (p.totalSalary() != 6400) {
            throw new Error("case 1 failed ");
        }

        //test case 2
        p.setEmployees(new String[]{"E001"});
        if (p.totalSalary() != 2300) {
            throw new Error("case 2 failed ");
        }

        //more tests...

        System.out.println("All tests passed");
    }
}

JUnit is a tool for automated testing of Java programs. Similar tools are available for other languages and for automating different types of testing.

@Test
public void testTotalSalary(){
    Payroll p = new Payroll();

    //test case 1
    p.setEmployees(new String[]{"E001", "E002"});
    assertEquals(p.totalSalary(), 6400);

    //test case 2
    p.setEmployees(new String[]{"E001"});
    assertEquals(p.totalSalary(), 2300);

    //more tests...
}

Most modern IDEs has integrated support for testing tools. The figure below shows the JUnit output when running some JUnit tests using the Eclipse IDE.

When writing JUnit tests for a class Foo, the common practice is to create a FooTest class, which will contain various test methods.

public class IntPair {
    int first;
    int second;

    public IntPair(int first, int second) {
        this.first = first;
        this.second = second;
    }

    public int intDivision() throws Exception {
        if (second == 0){
            throw new Exception("Divisor is zero");
        }
        return first/second;
    }

    @Override
    public String toString() {
        return first + "," + second;
    }
}

import org.junit.Test;
import org.junit.Assert;

public class IntPairTest {

    @Test
    public void testStringConversion() {
        Assert.assertEquals("4,7", new IntPair(4, 7).toString());
    }

    @Test
    public void intDivision_nonZeroDivisor_success() throws Exception {
        Assert.assertEquals(2, new IntPair(4, 2).intDivision());
        Assert.assertEquals(0, new IntPair(1, 2).intDivision());
        Assert.assertEquals(0, new IntPair(0, 5).intDivision());
    }

    @Test
    public void intDivision_zeroDivisor_exceptionThrown() {
        try {
            Assert.assertEquals(0, new IntPair(1, 0).intDivision());
            Assert.fail(); // the test should not reach this line
        } catch (Exception e) {
            Assert.assertEquals("Divisor is zero", e.getMessage());
        }
    }
}

Notes:

• Each test method is marked with a @Test annotation.
• Tests use Assert.assertEquals(expected, actual) methods to compare the expected output with the actual output. If they do not match, the test will fail. JUnit comes with other similar methods such as Assert.assertNull and Assert.assertTrue.
• Java code normally use camelCase for method names e.g., testStringConversion but when writing test methods, sometimes another convention is used: whatIsBeingTested_descriptionOfTestInputs_expectedOutcome e.g., intDivision_zeroDivisor_exceptionThrown
• There are several ways to verify the code throws the correct exception. The third test method in the example above shows one of the simpler methods. If the exception is thrown, it will be caught and further verified inside the catch block. But if it is not thrown as expected, the test will reach Assert.fail() line and will fail as a result.
• The easiest way to run JUnit tests is to do it via the IDE. For example, in Intellij you can right-click the folder containing test classes and choose 'Run all tests...'
• Optionally, you can use static imports to avoid having to specify Assert. everywhere.

  import static org.junit.Assert.assertEquals;
  //...
  @Test
  public void testStringConversion() {
      assertEquals("4,7", new IntPair(4, 7).toString());
  }
  

A milestone is the end of a stage which indicates a significant progress. We should take into account dependencies and priorities when deciding on the features to be delivered at a certain milestone.

In some projects, it is not practical to have a very detailed plan for the whole project due to the uncertainty and unavailability of required information. In such cases, we can use a high-level plan for the whole project and a detailed plan for the next few milestones.

A buffer is a time set aside to absorb any unforeseen delays. It is very important to include buffers in a software project schedule because effort/time estimations for software development is notoriously hard. However, do not inflate task estimates to create hidden buffers; have explicit buffers instead.  Reason: With explicit buffers it is easier to detect incorrect effort estimates which can serve as a feedback to improve future effort estimates.

Keeping track of project tasks (who is doing what, which tasks are ongoing, which tasks are done etc.) is an essential part of project management. In small projects it may be possible to track tasks using simple tools as online spreadsheets or general-purpose/light-weight tasks tracking tools such as Trello. Bigger projects need more sophisticated task tracking tools.

Issue trackers (sometimes called bug trackers) are commonly used to track task assignment and progress. Most online project management software such as GitHub, SourceForge, and BitBucket come with an integrated issue tracker.

A screenshot from the Jira Issue tracker software (Jira is part of the BitBucket project management tool suite):

A Work Breakdown Structure (WBS) depicts information about tasks and their details in terms of subtasks. When managing projects it is useful to divide the total work into smaller, well-defined units. Relatively complex tasks can be further split into subtasks. In complex projects a WBS can also include prerequisite tasks and effort estimates for each task.

The effort is traditionally measured in man hour/day/month i.e. work that can be done by one person in one hour/day/month. The Task ID is a label for easy reference to a task. Simple labeling is suitable for a small project, while a more informative labeling system can be adopted for bigger projects.

All tasks should be well-defined. In particular, it should be clear as to when the task will be considered done.

A Gantt chart is a 2-D bar-chart, drawn as time vs tasks (represented by horizontal bars).

A sample Gantt chart:

In a Gantt chart, a solid bar represents the main task, which is generally composed of a number of subtasks, shown as grey bars. The diamond shape indicates an important deadline/deliverable/milestone.

PERT (Program Evaluation Review Technique) chart uses a graphical technique to show the order/sequence of tasks. It is based on a simple idea of drawing a directed graph in which:

• Node or vertex captures the effort estimation of a task, and
• Arrow depicts the precedence between tasks

A PERT chart can help determine the following important information:

• The order of tasks.  In the example above, Final Testing cannot begin until all coding of individual subsystems have been completed.
• Which tasks can be done concurrently.  In the example above, the various subsystem designs can start independently once the High level design is completed.
• The shortest possible completion time.  In the example above, there is a path (indicated by the shaded boxes) from start to end that determines the shortest possible completion time.
• The Critical Path.  In the example above, the shortest possible path is also the critical path.

Given below are three commonly used team structures in software development. Irrespective of the team structure, it is a good practice to assign roles and responsibilities to different team members so that someone is clearly in charge of each aspect of the project. In comparison, the ‘everybody is responsible for everything’ approach can result in more chaos and hence slower progress.

Egoless team

In this structure, every team member is equal in terms of responsibility and accountability. When any decision is required, consensus must be reached. This team structure is also known as a democratic team structure. This team structure usually finds a good solution to a relatively hard problem as all team members contribute ideas.

However, the democratic nature of the team structure bears a higher risk of falling apart due to the absence of an authority figure to manage the team and resolve conflicts.

Chief programmer team

Frederick Brooks proposed that software engineers learn from the medical surgical team in an operating room. In such a team, there is always a chief surgeon, assisted by experts in other areas. Similarly, in a chief programmer team structure, there is a single authoritative figure, the chief programmer. Major decisions, e.g. system architecture, are made solely by him/her and obeyed by all other team members. The chief programmer directs and coordinates the effort of other team members. When necessary, the chief will be assisted by domain specialists e.g. business specialists, database expert, network technology expert, etc. This allows individual group members to concentrate solely on the areas where they have sound knowledge and expertise.

The success of such a team structure relies heavily on the chief programmer. Not only must he be a superb technical hand, he also needs good managerial skills. Under a suitably qualified leader, such a team structure is known to produce successful work. .

Strict hierarchy team

In the opposite extreme of an egoless team, a strict hierarchy team has a strictly defined organization among the team members, reminiscent of the military or bureaucratic government. Each team member only works on his assigned tasks and reports to a single “boss”.

In a large, resource-intensive, complex project, this could be a good team structure to reduce communication overhead.