The following description of the Joys of the Programming Craft was taken from Chapter 1 of the famous book The Mythical Man-Month, by Frederick P. Brooks.

Why is programming fun? What delights may its practitioner expect as his reward?

First is the sheer joy of making things. As the child delights in his mud pie, so the adult enjoys building things, especially things of his own design. I think this delight must be an image of God's delight in making things, a delight shown in the distinctness and newness of each leaf and each snowflake.

Second is the pleasure of making things that are useful to other people. Deep within, you want others to use your work and to find it helpful. In this respect the programming system is not essentially different from the child's first clay pencil holder "for Daddy's office."

Third is the fascination of fashioning complex puzzle-like objects of interlocking moving parts and watching them work in subtle cycles, playing out the consequences of principles built in from the beginning. The programmed computer has all the fascination of the pinball machine or the jukebox mechanism, carried to the ultimate.

Fourth is the joy of always learning, which springs from the nonrepeating nature of the task. In one way or another the problem is ever new, and its solver learns something: sometimes practical, sometimes theoretical, and sometimes both.

Finally, there is the delight of working in such a tractable medium. The programmer, like the poet, works only slightly removed from pure thought-stuff. He builds his castles in the air, from air, creating by the exertion of the imagination. Few media of creation are so flexible, so easy to polish and rework, so readily capable of realizing grand conceptual structures....

Yet the program construct, unlike the poet's words, is real in the sense that it moves and works, producing visible outputs separate from the construct itself. It prints results, draws pictures, produces sounds, moves arms. The magic of myth and legend has come true in our time. One types the correct incantation on a keyboard, and a display screen comes to life, showing things that never were nor could be.

Programming then is fun because it gratifies creative longings built deep within us and delights sensibilities you have in common with all men.

Not all is delight, however, and knowing the inherent woes makes it easier to bear them when they appear.

First, one must perform perfectly. The computer resembles the magic of legend in this respect, too. If one character, one pause, of the incantation is not strictly in proper form, the magic doesn't work. Human beings are not accustomed to being perfect, and few areas of human activity demand it. Adjusting to the requirement for perfection is, I think, the most difficult part of learning to program.

Next, other people set one's objectives, provide one's resources, and furnish one's information. One rarely controls the circumstances of his work, or even its goal. In management terms, one's authority is not sufficient for his responsibility. It seems that in all fields, however, the jobs where things get done never have formal authority commensurate with responsibility. In practice, actual (as opposed to formal) authority is acquired from the very momentum of accomplishment.

The dependence upon others has a particular case that is especially painful for the system programmer. He depends upon other people's programs. These are often maldesigned, poorly implemented, incompletely delivered (no source code or test cases), and poorly documented. So he must spend hours studying and fixing things that in an ideal world would be complete, available, and usable.

The next woe is that designing grand concepts is fun; finding nitty little bugs is just work. With any creative activity come dreary hours of tedious, painstaking labor, and programming is no exception.

Next, one finds that debugging has a linear convergence, or worse, where one somehow expects a quadratic sort of approach to the end. So testing drags on and on, the last difficult bugs taking more time to find than the first.

The last woe, and sometimes the last straw, is that the product over which one has labored so long appears to be obsolete upon (or before) completion. Already colleagues and competitors are in hot pursuit of new and better ideas. Already the displacement of one's thought-child is not only conceived, but scheduled.

This always seems worse than it really is. The new and better product is generally not available when one completes his own; it is only talked about. It, too, will require months of development. The real tiger is never a match for the paper one, unless actual use is wanted. Then the virtues of reality have a satisfaction all their own.

Of course the technological base on which one builds is always advancing. As soon as one freezes a design, it becomes obsolete in terms of its concepts. But implementation of real products demands phasing and quantizing. The obsolescence of an implementation must be measured against other existing implementations, not against unrealized concepts. The challenge and the mission are to find real solutions to real problems on actual schedules with available resources.

This then is programming, both a tar pit in which many efforts have floundered and a creative activity with joys and woes all its own. For many, the joys far outweigh the woes....

A software requirement specifies a need to be fulfilled by the software product.

A software project may be,

• a brownfield project i.e., develop a product to replace/update an existing software product
• a greenfield project i.e., develop a totally new system with no precedent

In either case, requirements need to be gathered, analyzed, specified, and managed.

Requirements come from stakeholders.

Identifying requirements is often not easy. For example, stakeholders may not be aware of their precise needs, may not know how to communicate their requirements correctly, may not be willing to spend effort in identifying requirements, etc.

Requirements can be divided into two in the following way:

1. Functional requirements specify what the system should do.
2. Non-functional requirements specify the constraints under which the system is developed and operated.

You may have to spend an extra effort in digging NFRs out as early as possible because,

1. NFRs are easier to miss e.g., stakeholders tend to think of functional requirements first
2. sometimes NFRs are critical to the success of the software. E.g. A web application that is too slow or that has low security is unlikely to succeed even if it has all the right functionality.

Here are some characteristics of well-defined requirements ^{[📖 zielczynski]}:

• Unambiguous
• Testable (verifiable)
• Clear (concise, terse, simple, precise)
• Correct
• Understandable
• Feasible (realistic, possible)
• Independent
• Necessary
• Implementation-free (i.e. abstract)

Besides these criteria for individual requirements, the set of requirements as a whole should be

• Consistent
• Non-redundant
• Complete

Requirements can be prioritized based on the importance and urgency, while keeping in mind the constraints of schedule, budget, staff resources, quality goals, and other constraints.

A common approach is to group requirements into priority categories. Note that all such scales are subjective, and stakeholders define the meaning of each level in the scale for the project at hand.

Some requirements can be discarded if they are considered ‘out of ’.

In a brainstorming session there are no "bad" ideas. The aim is to generate ideas; not to validate them. Brainstorming encourages you to "think outside the box" and put "crazy" ideas on the table without fear of rejection.

Surveys can be used to solicit responses and opinions from a large number of stakeholders regarding a current product or a new product.

Observing users in their natural work environment can uncover product requirements. Usage data of an existing system can also be used to gather information about how an existing system is being used, which can help in building a better replacement e.g. to find the situations where the user makes mistakes when using the current system.

Interviewing stakeholders and domain experts can produce useful information about project requirements.

Focus groups are a kind of informal interview within an interactive group setting. A group of people (e.g. potential users, beta testers) are asked about their understanding of a specific issue, process, product, advertisement, etc.

Prototyping can uncover requirements, in particular, those related to how users interact with the system. UI prototypes or mock ups are often used in brainstorming sessions, or in meetings with the users to get quick feedback from them.

Prototyping can be used for discovering as well as specifying requirements e.g. a UI prototype can serve as a specification of what to build.

Studying existing products can unearth shortcomings of existing solutions that can be addressed by a new product. Product manuals and other forms of documentation of an existing system can tell us how the existing solutions work.

A textual description (i.e. prose) can be used to describe requirements. Prose is especially useful when describing abstract ideas such as the vision of a product.

Avoid using lengthy prose to describe requirements; they can be hard to follow.

A common format for writing user stories is:

You can write user stories on index cards or sticky notes, and arrange them on walls or tables, to facilitate planning and discussion. Alternatively, you can use a software (e.g., GitHub Project Boards, Trello, Google Docs, ...) to manage user stories digitally.

User stories in use

With sticky notes

^{[credit: https://www.flickr.com/photos/jakuza/2682466984/]}

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With paper

^{[credit: https://www.flickr.com/photos/jakuza/with/2726048607/]}

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With software

^{[credit: https://commons.wikimedia.org/wiki/File:User_Story_Map_in_Action.png]}

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The {benefit} can be omitted if it is obvious.

It is recommended to confirm there is a concrete benefit even if you omit it from the user story. If not, you could end up adding features that have no real benefit.

You can add more characteristics to the {user role} to provide more context to the user story.

You can write user stories at various levels. High-level user stories, called epics (or themes) cover bigger functionality. You can then break down these epics to multiple user stories of normal size.

You can add conditions of satisfaction to a user story to specify things that need to be true for the user story implementation to be accepted as ‘done’.

Other useful info that can be added to a user story includes (but not limited to)

• Priority: how important the user story is
• Size: the estimated effort to implement the user story
• Urgency: how soon the feature is needed

User stories capture user requirements in a way that is convenient for , , and .

[User stories] strongly shift the focus from writing about features to discussing them. In fact, these discussions are more important than whatever text is written. _{[Mike Cohn, MountainGoat Software 🔗]}

User stories differ from mainly in the level of detail. User stories should only provide enough details to make a reasonably low risk estimate of how long the user story will take to implement. When the time comes to implement the user story, the developers will meet with the customer face-to-face to work out a more detailed description of the requirements. [more...]

User stories can capture non-functional requirements too because even NFRs must benefit some stakeholder.

While use cases can be recorded on in the initial stages, an online tool is more suitable for longer-term management of user stories, especially if the team is not .

A use case describes an interaction between the user and the system for a specific functionality of the system.

System: Online Banking System (OBS)
Use case: UC23 - Transfer Money
Actor: User
MSS:
  1. User chooses to transfer money.
  2. OBS requests for details of the transfer.
  3. User enters the requested details.
  4. OBS requests for confirmation.
  5. User confirms.
  6. OBS transfers the money and displays the new account balance.
  Use case ends.

    

    


    
    

Extensions:
  3a. OBS detects an error in the entered data.
      3a1. OBS requests for the correct data.
      3a2. User enters new data.
      Steps 3a1-3a2 are repeated until the data entered are correct.
      Use case resumes from step 4.

  3b. User requests to effect the transfer in a future date.
      3b1. OBS requests for confirmation.
      3b2. User confirms future transfer.
      Use case ends.

  *a. At any time, User chooses to cancel the transfer.
      *a1. OBS requests to confirm the cancellation.
      *a2. User confirms the cancellation.
      Use case ends.

    

    


    
    

UML includes a diagram type called use case diagrams that can illustrate use cases of a system visually, providing a visual ‘table of contents’ of the use cases of a system.

In the example on the right, note how use cases are shown as ovals and user roles relevant to each use case are shown as stick figures connected to the corresponding ovals.

Use cases capture the functional requirements of a system.

A use case is an interaction between a system and its actors.

Actors in Use Cases

A use case can involve multiple actors.

An actor can be involved in many use cases.

A single person/system can play many roles.

Many persons/systems can play a single role.

Use cases can be specified at various levels of detail.

While modeling user-system interactions,

• Start with high level use cases and progressively work toward lower level use cases.
• Be mindful of which level of detail you are working at and not to mix use cases of different levels.

Writing use case steps

Extensions are "add-on"s to the MSS that describe exceptional/alternative flow of events. They describe variations of the scenario that can happen if certain things are not as expected by the MSS. Extensions appear below the MSS.

This example adds some extensions to the use case in the previous example.

System: Online Banking System (OBS)
Use case: UC23 - Transfer Money
Actor: User
MSS:
  1. User chooses to transfer money.
  2. OBS requests for details of the transfer.
  3. User enters the requested details.
  4. OBS requests for confirmation.
  5. User confirms.
  6. OBS transfers the money and displays the new account balance.
  Use case ends.

    

    


    
    

Extensions:
  3a. OBS detects an error in the entered data.
      3a1. OBS requests for the correct data.
      3a2. User enters new data.
      Steps 3a1-3a2 are repeated until the data entered are correct.
      Use case resumes from step 4.
  
  3b. User requests to effect the transfer in a future date.
      3b1. OBS requests for confirmation.
      3b2. User confirms future transfer.
      Use case ends.
  
  *a. At any time, User chooses to cancel the transfer.
      *a1. OBS requests to confirm the cancellation.
      *a2. User confirms the cancellation.
      Use case ends.

  *b. At any time, 120 seconds lapse without any input from the User.
      *b1. OBS cancels the transfer.
      *b2. OBS informs the User of the cancellation.
      Use case ends.

    

    


    
    

Note that the numbering style is not a universal rule but a widely used convention. Based on that convention,

• either of the extensions marked 3a. and 3b. can happen just after step 3 of the MSS.
• the extension marked as *a. can happen at any step (hence, the *).

When separating extensions from the MSS, keep in mind that the MSS should be self-contained. That is, the MSS should give us a complete usage scenario.

Also note that it is not useful to mention events such as power failures or system crashes as extensions because the system cannot function beyond such catastrophic failures.

In use case diagrams you can use the <<extend>> arrows to show extensions. Note the direction of the arrow is from the extension to the use case it extends and the arrow uses a dashed line.

You can use actor generalization in use case diagrams using a symbol similar to that of UML notation for inheritance.

In this example, actor Blogger can do all the use cases the actor Guest can do, as a result of the actor generalization relationship given in the diagram.

Do not over-complicate use case diagrams by trying to include everything possible. A use case diagram is a brief summary of the use cases that is used as a starting point. Details of the use cases can be given in the use case descriptions.

Some include ‘System’ as an actor to indicate that something is done by the system itself without being initiated by a user or an external system.

The diagram below can be used to indicate that the system generates daily reports at midnight.

However, others argue that only use cases providing value to an external user/system should be shown in the use case diagram. For example, they argue that view daily report should be the use case and generate daily report is not to be shown in the use case diagram because it is simply something the system has to do to support the view daily report use case.

You are recommended to follow the latter view (i.e. not to use System as a user). Limit use cases for modeling behaviors that involve an external actor.

UML is not very specific about the text contents of a use case. Hence, there are many styles for writing use cases. For example, the steps can be written as a continuous paragraph.

Use cases should be easy to read. Note that there is no strict rule about writing all details of all steps or a need to use all the elements of a use case.

There are some advantages of documenting system requirements as use cases:

• Because they use a simple notation and plain English descriptions, they are easy for users to understand and give feedback.
• They decouple user intention from mechanism (note that use cases should not include UI-specific details), allowing the system designers more freedom to optimize how a functionality is provided to a user.
• Identifying all possible extensions encourages us to consider all situations that a software product might face during its operation.
• Separating typical scenarios from special cases encourages us to optimize the typical scenarios.

One of the main disadvantages of use cases is that they are not good for capturing requirements that do not involve a user interacting with the system. Hence, they should not be used as the sole means to specify requirements.

A supplementary requirements section can be used to capture requirements that do not fit elsewhere. Typically, this is where most Non-Functional Requirements will be listed.

Software design has two main aspects:

• Product/external design: designing the external behavior of the product to meet the users' requirements. This is usually done by product designers with input from business analysts, user experience experts, user representatives, etc.
• Implementation/internal design: designing how the product will be implemented to meet the required external behavior. This is usually done by software architects and software engineers.

The guiding principle of abstraction is that only details that are relevant to the current perspective or the task at hand need to be considered. As 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. That is where abstraction can help.

Data abstraction: abstracting away the lower level data items and thinking in terms of bigger entities

Control abstraction: abstracting away details of the actual control flow to focus on tasks at a higher level

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

Abstraction is a general concept that is not limited to just data or control abstractions.

A model is a representation of something else.

A model provides a simpler view of a complex entity because a model captures only a selected aspect. This omission of some aspects implies models are abstractions.

Multiple models of the same entity may be needed to capture it fully.

In software development, models are useful in several ways:

a) To analyze a complex entity related to software development.

b) To communicate information among stakeholders. Models can be used as a visual aid in discussions and documentation.

c) As a blueprint for creating software. Models can be used as instructions for building software.

The following diagram uses the class diagram notation to show the different types of UML diagrams.

Object-Oriented Programming (OOP) is a programming paradigm. A programming paradigm guides programmers to analyze programming problems, and structure programming solutions, in a specific way.

Programming languages have traditionally divided the world into two parts—data and operations on data. Data is static and immutable, except as the operations may change it. The procedures and functions that operate on data have no lasting state of their own; they’re useful only in their ability to affect data.

This division is, of course, grounded in the way computers work, so it’s not one that you can easily ignore or push aside. Like the equally pervasive distinctions between matter and energy and between nouns and verbs, it forms the background against which you work. At some point, all programmers—even object-oriented programmers—must lay out the data structures that their programs will use and define the functions that will act on the data.

With a procedural programming language like C, that’s about all there is to it. The language may offer various kinds of support for organizing data and functions, but it won’t divide the world any differently. Functions and data structures are the basic elements of design.

Object-oriented programming doesn’t so much dispute this view of the world as restructure it at a higher level. It groups operations and data into modular units called objects and lets you combine objects into structured networks to form a complete program. In an object-oriented programming language, objects and object interactions are the basic elements of design.

_{-- Object-Oriented Programming with Objective-C, Apple}

Some other examples of programming paradigms are:

Some programming languages support multiple paradigms.

Every object has both state (data) and behavior (operations on data). In that, they’re not much different from ordinary physical objects. It’s easy to see how a mechanical device, such as a pocket watch or a piano, embodies both state and behavior. But almost anything that’s designed to do a job does, too. Even simple things with no moving parts such as an ordinary bottle combine state (how full the bottle is, whether or not it’s open, how warm its contents are) with behavior (the ability to dispense its contents at various flow rates, to be opened or closed, to withstand high or low temperatures).

It’s this resemblance to real things that gives objects much of their power and appeal. They can not only model components of real systems, but equally as well fulfill assigned roles as components in software systems.

_{-- Object-Oriented Programming with Objective-C, Apple}

Object Oriented Programming (OOP) views the world as a network of interacting objects.

OOP solutions try to create a similar object network inside the computer’s memory – a sort of virtual simulation of the corresponding real world scenario – so that a similar result can be achieved programmatically.

OOP does not demand that the virtual world object network follow the real world exactly.

Every object has both state (data) and behavior (operations on data).

Every object has an interface and an implementation.

Every real world object has:

• an interface through which other objects can interact with it
• an implementation that supports the interface but may not be accessible to the other object

Similarly, every object in the virtual world has an interface and an implementation.

Objects interact by sending messages. Both real world and virtual world object interactions can be viewed as objects sending messages to each other. The message can result in the sender object receiving a response and/or the receiver object’s state being changed. Furthermore, the result can vary based on which object received the message, even if the message is identical (see rows 1 and 2 in the example below).

The concept of Objects in OOP is an abstraction mechanism because it allows us to abstract away the lower level details and work with bigger granularity entities i.e. ignore details of data formats and the method implementation details and work at the level of objects.

Encapsulation protects an implementation from unintended actions and from inadvertent access.
_{-- Object-Oriented Programming with Objective-C, Apple}

An object is an encapsulation of some data and related behavior in terms of two aspects:

1. The packaging aspect: An object packages data and related behavior together into one self-contained unit.

2. The information hiding aspect: The data in an object is hidden from the outside world and are only accessible using the object's interface.

Writing an OOP program is essentially writing instructions that the computer will use to,

1. create the virtual world of the object network, and
2. provide it the inputs to produce the outcome you want.

A class contains instructions for creating a specific kind of objects. It turns out sometimes multiple objects keep the same type of data and have the same behavior because they are of the same kind. Instructions for creating a 'kind' (or ‘class’) of objects can be done once and those same instructions can be used to objects of that kind. You call such instructions a Class.

While all objects of a class have the same attributes, each object has its own copy of the attribute value.

However, some attributes are not suitable to be maintained by individual objects. Instead, they should be maintained centrally, shared by all objects of the class. They are like ‘global variables’ but attached to a specific class. Such variables whose value is shared by all instances of a class are called class-level attributes.

Similarly, when a normal method is being called, a message is being sent to the receiving object and the result may depend on the receiving object.

However, there can be methods related to a specific class but not suitable for sending messages to a specific object of that class. Such methods that are called using the class instead of a specific instance are called class-level methods.

Class-level attributes and methods are collectively called class-level members (also called static members sometimes because some programming languages use the keyword static to identify class-level members). They are to be accessed using the class name rather than an instance of the class.

Objects in an OO solution need to be connected to each other to form a network so that they can interact with each other. Such connections between objects are called associations.

Associations in an object structure can change over time.

Associations among objects can be generalized as associations between the corresponding classes too.

Implementing associations

You use instance level variables to implement associations.

When two classes are linked by an association, it does not necessarily mean the two objects taking part in an instance of the association knows about (i.e., has a reference to) each other. The concept of which object in the association knows about the other object is called navigability.

Navigability can be unidirectional or bidirectional. Suppose there is an association between the classes Box and Rope, and the Box object b and the Rope object r is taking part in one instance of that association.

• Unidirectional: If the navigability is from Box to Rope, b will have a reference to r but r will not have a reference to b. Similarly, if the navigability is in the other direction, r will have a reference to b but b will not have a reference to r.
• Bidirectional: b will have a reference to r and r will have a reference to b i.e., the two objects will be pointing to each other for the same single instance of the association.

Note that two unidirectional associations in opposite directions do not add up to a single bidirectional association.

class Person {
    Cat pet;
    //...
}

class Cat{
    Person owner;
    //...
}

    

    


    
    

class Person:

  def __init__(self):
    self.pet = None  # a Cat object


class Cat:

  def __init__(self):
    self.owner = None  # a Person object

    

    


    
    

class Person {
    Cat pet;
    //...
}

class Cat{
    Person breeder;
    //...
}

    

    


    
    

class Person:

  def __init__(self):
    self.pet = None  # a Cat object


class Cat:

  def __init__(self):
    self.breeder = None  # a Person object

    

    


    
    

Multiplicity is the aspect of an OOP solution that dictates how many objects take part in each association.

Implementing multiplicity

A normal instance-level variable gives us a 0..1 multiplicity (also called optional associations) because a variable can hold a reference to a single object or null.

class Logic {
    Minefield minefield;
    // ...
}

class Minefield {
    //...
}

    

    


    
    

class Logic:
  
  def __init__(self):
    self.minefield = None
    
  # ...


class Minefield:
  # ...

    

    


    
    

A variable can be used to implement a 1 multiplicity too (also called compulsory associations).

class Logic {
    ConfigGenerator cg = new ConfigGenerator();
    ...
}

    

    


    
    

class Logic:

  def __init__(self):
    self.config_gen = ConfigGenerator()

    

    


    
    

Bidirectional associations require matching variables in both classes.

class Foo {
    Bar bar;
    //...
}

class Bar {
    Foo foo;
    //...
}


    

    


    
    

class Foo:
  
  def __init__(self, bar):
    self.bar = bar;


class Bar:
  
  def __init__(self, foo):
    self.foo = foo;
    

    

    


    
    

To implement other multiplicities, choose a suitable data structure such as Arrays, ArrayLists, HashMaps, Sets, etc.

class Minefield {
    Cell[][] cell;
    ...
}

    

    


    
    

class Minefield:
  
  def __init__(self):
    self.cells = {1:[], 2:[], 3:[]}

    

    


    
    

In the context of OOP associations, a dependency is a need for one class to depend on another without having a direct association in the same direction. Reason for the exclusion: If there is an association from class Foo to class Bar (i.e., navigable from Foo to Bar), that means Foo is obviously dependent on Bar and hence there is no point in mentioning dependency specifically. In other words, we are only concerned about non-obvious dependencies here. One cause of such dependencies is interactions between objects that do not have a long-term link between them.

class Foo {
    
    int calculate(Bar bar) {
        return bar.getValue();
    }
}

class Bar {
    int value;
    
    int getValue() {
        return value;
    }
}

    

    


    
    

class Foo:
    
    def calculate(self, bar):
        return bar.value;

class Bar:
    
    def __init__(self, value):
      self.value = value

    

    


    
    

A composition is an association that represents a strong whole-part relationship. When the whole is destroyed, parts are destroyed too i.e., the part should not exist without being attached to a whole.

Composition also implies that there cannot be cyclical links.

Whether a relationship is a composition can depend on the context.

A common use of composition is when parts of a big class are carved out as smaller classes for the ease of managing the internal design. In such cases, the classes extracted out still act as parts of the bigger class and the outside world has no business knowing about them.

Cascading deletion alone is not sufficient for composition. Suppose there is a design in which Person objects are attached to Task objects and the former get deleted whenever the latter is deleted. This fact alone does not mean there is a composition relationship between the two classes. For it to be composition, a Person must be an integral part of a Task in the context of that association, at the concept level (not simply at implementation level).

Identifying and keeping track of composition relationships in the design has benefits such as helping to maintain the data integrity of the system. For example, when you know that a certain relationship is a composition, you can take extra care in your implementation to ensure that when the whole object is deleted, all its parts are deleted too.

Implementing composition

Composition is implemented using a normal variable. If correctly implemented, the ‘part’ object will be deleted when the ‘whole’ object is deleted. Ideally, the ‘part’ object may not even be visible to clients of the ‘whole’ object.

class Email {
    private Subject subject;
  ...
}

    

    


    
    

class Email:

  def __init__(self):
    self.__subject = Subject()

    

    


    
    

Aggregation represents a container-contained relationship. It is a weaker relationship than composition.

Implementing aggregation

Implementation is similar to that of composition except the containee object can exist even after the container object is deleted.

class Team {
    Person leader;
    ...
    void setLeader(Person p) {
        leader = p;
    }
}

    

    


    
    

class Team:
  
  def __init__(self):
    self.__leader = None
    
  def set_leader(self, person):
    self.__leader = person

    

    


    
    

The OOP concept Inheritance allows you to define a new class based on an existing class.

• Other names for Base class: Parent class, Superclass
• Other names for Derived class: Child class, Subclass, Extended class

A superclass is said to be more general than the subclass. Conversely, a subclass is said to be more specialized than the superclass.

Applying inheritance on a group of similar classes can result in the common parts among classes being extracted into more general classes.

Inheritance implies the derived class can be considered as a sub-type of the base class (and the base class is a super-type of the derived class), resulting in an is a relationship.

Inheritance does not necessarily mean a sub-type relationship exists. However, the two often go hand-in-hand. For simplicity, at this point let us assume inheritance implies a sub-type relationship.

Inheritance relationships through a chain of classes can result in inheritance hierarchies (aka inheritance trees).

Two inheritance hierarchies/trees are given below. Note that the triangle points to the parent class. Observe how the Parrot is a Bird as well as it is an Animal.

Multiple Inheritance is when a class inherits directly from multiple classes. Multiple inheritance among classes is allowed in some languages (e.g., Python, C++) but not in other languages (e.g., Java, C#).

The Honey class inherits from the Food class and the Medicine class because honey can be consumed as a food as well as a medicine (in some oriental medicine practices). Similarly, a Car is a Vehicle, an Asset and a Liability.

Method overriding is when a sub-class changes the behavior inherited from the parent class by re-implementing the method. Overridden methods have the same name, same type signature, and same return type.

Method overloading is when there are multiple methods with the same name but different type signatures. Overloading is used to indicate that multiple operations do similar things but take different parameters.

Well-written applications include error-handling code that allows them to recover gracefully from unexpected errors. When an error occurs, the application may need to request user intervention, or it may be able to recover on its own. In extreme cases, the application may log the user off or shut down the system. -- _Microsoft

Exceptions are used to deal with 'unusual' but not entirely unexpected situations that the program might encounter at runtime.

Most languages allow code that encountered an "exceptional" situation to encapsulate details of the situation in an Exception object and throw/raise that object so that another piece of code can catch it and deal with it. This is especially useful when the code that encountered the unusual situation does not know how to deal with it.

The extract below from the -- Java Tutorial (with slight adaptations) explains how exceptions are typically handled.

Advantages of exception handling in this way:

• The ability to propagate error information through the call stack.
• The separation of code that deals with 'unusual' situations from the code that does the 'usual' work.

In general, use exceptions only for 'unusual' conditions. Use normal return statements to pass control to the caller for conditions that are 'normal'.

Professional software engineers often write code using Integrated Development Environments (IDEs). IDEs support most development-related work within the same tool (hence, the term integrated).

An IDE generally consists of:

• A source code editor that includes features such as syntax coloring, auto-completion, easy code navigation, error highlighting, and code-snippet generation.
• A compiler and/or an interpreter (together with other build automation support) that facilitates the compilation/linking/running/deployment of a program.
• A debugger that allows the developer to execute the program one step at a time to observe the run-time behavior in order to locate bugs.
• Other tools that aid various aspects of coding e.g. support for automated testing, drag-and-drop construction of UI components, version management support, simulation of the target runtime platform, and modeling support.

Examples of popular IDEs:

• Java: Eclipse, Intellij IDEA, NetBeans
• C#, C++: Visual Studio
• Swift: XCode
• Python: PyCharm

Some web-based IDEs have appeared in recent times too e.g., Amazon's Cloud9 IDE.

Some experienced developers, in particular those with a UNIX background, prefer lightweight yet powerful text editors with scripting capabilities (e.g. Emacs) over heavier IDEs.

Debugging is the process of discovering defects in the program. Here are some approaches to debugging:

• Bad -- By inserting temporary print statements: This is an ad-hoc approach in which print statements are inserted in the program to print information relevant to debugging, such as variable values. e.g. Exiting process() method, x is 5.347. This approach is not recommended due to these reasons:
  • Incurs extra effort when inserting and removing the print statements.
  • These extraneous program modifications increase the risk of introducing errors into the program.
  • These print statements, if not removed promptly after the debugging, may even appear unexpectedly in the production version.
• Bad -- By manually tracing through the code: Otherwise known as ‘eye-balling’, this approach doesn't have the cons of the previous approach, but it too is not recommended (other than as a 'quick try') due to these reasons:
  • It is a difficult, time consuming, and error-prone technique.
  • If you didn't spot the error while writing the code, you might not spot the error when reading the code either.
• Good -- Using a debugger: A debugger tool allows you to pause the execution, then step through the code one statement at a time while examining the internal state if necessary. Most IDEs come with an inbuilt debugger. This is the recommended approach for debugging.

Combining parts of a software product to form a whole is called integration. It is also one of the most troublesome tasks and it rarely goes smoothly.

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

Always code as if the person who ends up maintaining your code will be a violent psychopath who knows where you live. _{-- Martin Golding}

Production code needs to be of high quality. Given how the world is becoming increasingly dependent on software, poor quality code is something no one can afford to tolerate.

Programs should be written and polished until they acquire publication quality. _{--Niklaus Wirth}

Among various dimensions of code quality, such as run-time efficiency, security, and robustness, one of the most important is understandability. This is because in any non-trivial software project, code needs to be read, understood, and modified by other developers later on. Even if you do not intend to pass the code to someone else, code quality is still important because you will become a 'stranger' to your own code someday.

int subsidy() {
    int subsidy;
    if (!age) {
        if (!sub) {
            if (!notFullTime) {
                subsidy = 500;
            } else {
                subsidy = 250;
            }
        } else {
            subsidy = 250;
        }
    } else {
        subsidy = -1;
    }
    return subsidy;
}

    

    


    
    

int calculateSubsidy() {
    int subsidy;
    if (isSenior) {
        subsidy = REJECT_SENIOR;
    } else if (isAlreadySubsidized) {
        subsidy = SUBSIDIZED_SUBSIDY;
    } else if (isPartTime) {
        subsidy = FULLTIME_SUBSIDY * RATIO;
    } else {
        subsidy = FULLTIME_SUBSIDY;
    }
    return subsidy;
}

    

    


    
    

def calculate_subs():
    if not age:
        if not sub:
            if not not_fulltime:
                subsidy = 500
            else:
                subsidy = 250
        else:
            subsidy = 250
    else:
        subsidy = -1
    return subsidy

    

    


    
    

def calculate_subsidy():
    if is_senior:
        return REJECT_SENIOR
    elif is_already_subsidized:
        return SUBSIDIZED_SUBSIDY
    elif is_parttime:
        return FULLTIME_SUBSIDY * RATIO
    else:
        return FULLTIME_SUBSIDY

    

    


    
    

Be wary when a method is longer than the computer screen, and take corrective action when it goes beyond 30 LOC (lines of code). The bigger the haystack, the harder it is to find a needle.

If you need more than 3 levels of indentation, you're screwed anyway, and should fix your program. _{--Linux 1.3.53 Coding Style}

In particular, avoid arrowhead style code.

int subsidy() {
    int subsidy;
    if (!age) {
        if (!sub) {
            if (!notFullTime) {
                subsidy = 500;
            } else {
                subsidy = 250;
            }
        } else {
            subsidy = 250;
        }
    } else {
        subsidy = -1;
    }
    return subsidy;
}

    

    


    
    

int calculateSubsidy() {
    int subsidy;
    if (isSenior) {
        subsidy = REJECT_SENIOR;
    } else if (isAlreadySubsidized) {
        subsidy = SUBSIDIZED_SUBSIDY;
    } else if (isPartTime) {
        subsidy = FULLTIME_SUBSIDY * RATIO;
    } else {
        subsidy = FULLTIME_SUBSIDY;
    }
    return subsidy;
}

    

    


    
    

def calculate_subs():
    if not age:
        if not sub:
            if not not_fulltime:
                subsidy = 500
            else:
                subsidy = 250
        else:
            subsidy = 250
    else:
        subsidy = -1
    return subsidy

    

    


    
    

def calculate_subsidy():
    if is_senior:
        return REJECT_SENIOR
    elif is_already_subsidized:
        return SUBSIDIZED_SUBSIDY
    elif is_parttime:
        return FULLTIME_SUBSIDY * RATIO
    else:
        return FULLTIME_SUBSIDY

    

    


    
    

Avoid complicated expressions, especially those having many negations and nested parentheses. If you must evaluate complicated expressions, have it done in steps (i.e. calculate some intermediate values first and use them to calculate the final value).

return ((length < MAX_LENGTH) || (previousSize != length))
        && (typeCode == URGENT);

    

    


    
    

boolean isWithinSizeLimit = length < MAX_LENGTH;
boolean isSameSize = previousSize != length;
boolean isValidCode = isWithinSizeLimit || isSameSize;

boolean isUrgent = typeCode == URGENT;

return isValidCode && isUrgent;

    

    


    
    

return ((length < MAX_LENGTH) or (previous_size != length)) and (type_code == URGENT)

    

    


    
    

is_within_size_limit = length < MAX_LENGTH
is_same_size = previous_size != length
is_valid_code = is_within_size_limit or is_same_size

is_urgent = type_code == URGENT

return is_valid_code and is_urgent

    

    


    
    

The competent programmer is fully aware of the strictly limited size of his own skull; therefore he approaches the programming task in full humility, and among other things he avoids clever tricks like the plague. _{-- Edsger Dijkstra}

When the code has a number that does not explain the meaning of the number, it is called a "magic number" (as in "the number appears as if by magic"). Using a makes the code easier to understand because the name tells us more about the meaning of the number.

return 3.14236;
...
return 9;

    

    


    
    

static final double PI = 3.14236;
static final int MAX_SIZE = 10;
...
return PI;
...
return MAX_SIZE - 1;

    

    


    
    

return 3.14236
...
return 9

    

    


    
    

PI = 3.14236
MAX_SIZE = 10
...
return PI
...
return MAX_SIZE - 1

    

    


    
    

Similarly, you can have ‘magic’ values of other data types.

return "Error 1432"; // A magic string!

    

    


    
    

return "Error 1432" # A magic string!

    

    


    
    

In general, try to avoid any magic literals.

One essential way to improve code quality is to follow a consistent style. That is why software engineers follow a strict coding standard (aka style guide).

The aim of a coding standard is to make the entire code base look like it was written by one person. A coding standard is usually specific to a programming language and specifies guidelines such as the locations of opening and closing braces, indentation styles and naming styles (e.g. whether to use Hungarian style, Pascal casing, Camel casing, etc.). It is important that the whole team/company uses the same coding standard and that the standard is generally not inconsistent with typical industry practices. If a company's coding standard is very different from what is typically used in the industry, new recruits will take longer to get used to the company's coding style.

IDEs can help to enforce some parts of a coding standard e.g. indentation rules.

Proper naming improves the readability of code. It also reduces bugs caused by ambiguities regarding the intent of a variable or a method.

There are only two hard things in Computer Science: cache invalidation and naming things. _{-- Phil Karlton}

Every system is built from a domain-specific language designed by the programmers to describe that system. Functions are the verbs of that language, and classes are the nouns.
_{-- Robert C. Martin, Clean Code: A Handbook of Agile Software Craftsmanship}

Use nouns for classes/variables and verbs for methods/functions.

Distinguish clearly between single-valued and multi-valued variables.

Person student;
ArrayList<Person> students;

    

    


    
    

name = 'Jim'
names = ['Jim', 'Alice']

    

    


    
    

Use correct spelling in names. Avoid 'texting-style' spelling. Avoid foreign language words, slang, and names that are only meaningful within specific contexts/times e.g. terms from private jokes, a TV show currently popular in your country.

Good code is its own best documentation. As you’re about to add a comment, ask yourself, ‘How can I improve the code so that this comment isn’t needed?’ Improve the code and then document it to make it even clearer. _{-- Steve McConnell, Author of Clean Code}

Some think commenting heavily increases the 'code quality'. That is not so. Avoid writing comments to explain bad code. Improve the code to make it self-explanatory.

If the code is self-explanatory, refrain from repeating the description in a comment just for the sake of 'good documentation'.

//increment x
x++;

//trim the input
trimInput();

    

    


    
    

# increment x
x = x + 1

# trim the input
trim_input()

    

    


    
    

Do not write comments as if they are private notes to yourself. Instead, write them well enough to be understood by another programmer. One type of comment that is almost always useful is the header comment that you write for a class or an operation to explain its purpose.

// a quick trim function used to fix bug I detected overnight
void trimInput() {
    ....
}

    

    


    
    

/** Trims the input of leading and trailing spaces */
void trimInput() {
    ....
}

    

    


    
    

def trim_input():
"""a quick trim function used to fix bug I detected overnight"""
    ...

    

    


    
    

def trim_input():
"""Trim the input of leading and trailing spaces"""
    ...

    

    


    
    

The first version of the code you write may not be of production quality. It is OK to first concentrate on making the code work, rather than worry over the quality of the code, as long as you improve the quality later. This process of improving a program's internal structure in small steps without modifying its external behavior is called refactoring.

• Refactoring is not rewriting: Discarding poorly-written code entirely and re-writing it from scratch is not refactoring because refactoring needs to be done in small steps.
• Refactoring is not bug fixing: By definition, refactoring is different from bug fixing or any other modifications that alter the external behavior (e.g. adding a feature) of the component in concern.

Given below are two common refactorings (more).

if (isSpecialDeal()) {
    total = price * 0.95;
    send();
} else {
    total = price * 0.98;
    send();
}

    

    


    
    

if (isSpecialDeal()) {
    total = price * 0.95;
} else {
    total = price * 0.98;
}
send();


    

    


    
    

if is_special_deal:
    total = price * 0.95
    send()
else:
    total = price * 0.98
    send()

    

    


    
    

if is_special_deal:
    total = price * 0.95
else:
    total = price * 0.98

send()

    

    


    
    

void printOwing() {
    printBanner();

    // print details
    System.out.println("name:	" + name);
    System.out.println("amount	" + getOutstanding());
}

    

    


    
    

void printOwing() {
    printBanner();
    printDetails(getOutstanding());
}

void printDetails(double outstanding) {
    System.out.println("name:	" + name);
    System.out.println("amount	" + outstanding);
}

    

    


    
    

def print_owing():
    print_banner()

    # print details
    print("name:	" + name)
    print("amount	" + get_outstanding())

    

    


    
    

def print_owing():
    print_banner()
    print_details(get_outstanding())

def print_details(amount):
    print("name:	" + name)
    print("amount	" + amount)

    

    


    
    

Some IDEs have builtin support for basic refactorings such as automatically renaming a variable/method/class in all places it has been used.

Refactoring, even if done with the aid of an IDE, may still result in regressions. Therefore, each small refactoring should be followed by regression testing.

Reuse is a major theme in software engineering practices. By reusing tried-and-tested components, the robustness of a new software system can be enhanced while reducing the manpower and time requirement. Reusable components come in many forms; it can be reusing a piece of code, a subsystem, or a whole software.

While you may be tempted to use many libraries/frameworks/platforms that seem to crop up on a regular basis and promise to bring great benefits, note that there are costs associated with reuse. Here are some:

• The reused code may be an overkill (think using a sledgehammer to crack a nut), increasing the size of, and/or degrading the performance of, your software.
• The reused software may not be mature/stable enough to be used in an important product. That means the software can change drastically and rapidly, possibly in ways that break your software.
• Non-mature software has the risk of dying off as fast as they emerged, leaving you with a dependency that is no longer maintained.
• The license of the reused software (or its dependencies) restrict how you can use/develop your software.
• The reused software might have bugs, missing features, or security vulnerabilities that are important to your product, but not so important to the maintainers of that software, which means those flaws will not get fixed as fast as you need them to.
• Malicious code can sneak into your product via compromised dependencies.

An Application Programming Interface (API) specifies the interface through which other programs can interact with a software component. It is a contract between the component and its clients.

When developing large systems, if you define the API of each component early, the development team can develop the components in parallel because the future behavior of the other components are now more predictable.

A library is a collection of modular code that is general and can be used by other programs.

These are the typical steps required to use a library:

1. Read the documentation to confirm that its functionality fits your needs.
2. Check the license to confirm that it allows reuse in the way you plan to reuse it. For example, some libraries might allow non-commercial use only.
3. Download the library and make it accessible to your project. Alternatively, you can configure your to do it for you.
4. Call the library API from your code where you need to use the library's functionality.

The overall structure and execution flow of a specific category of software systems can be very similar. The similarity is an opportunity to reuse at a high scale.

A software framework is a reusable implementation of a software (or part thereof) providing generic functionality that can be selectively customized to produce a specific application.

Some frameworks provide a complete implementation of a default behavior which makes them immediately usable.

A framework facilitates the adaptation and customization of some desired functionality.

Some frameworks cover only a specific component or an aspect.

Although both frameworks and libraries are reuse mechanisms, there are notable differences:

• Libraries are meant to be used ‘as is’ while frameworks are meant to be customized/extended. e.g., writing plugins for Eclipse so that it can be used as an IDE for different languages (C++, PHP, etc.), adding modules and themes to Drupal, and adding test cases to JUnit.

• Your code calls the library code while the framework code calls your code. Frameworks use a technique called inversion of control, aka the “Hollywood principle” (i.e. don’t call us, we’ll call you!). That is, you write code that will be called by the framework, e.g. writing test methods that will be called by the JUnit framework. In the case of libraries, your code calls libraries.

A platform provides a runtime environment for applications. A platform is often bundled with various libraries, tools, frameworks, and technologies in addition to a runtime environment but the defining characteristic of a software platform is the presence of a runtime environment.

Cloud computing is the delivery of computing as a service over the network, rather than a product running on a local machine. This means the actual hardware and software is located at a remote location, typically, at a large server farm, while users access them over the network. Maintenance of the hardware and software is managed by the cloud provider while users typically pay for only the amount of services they use. This model is similar to the consumption of electricity; the power company manages the power plant, while the consumers pay them only for the electricity used. The cloud computing model optimizes hardware and software utilization and reduces the cost to consumers. Furthermore, users can scale up/down their utilization at will without having to upgrade their hardware and software. The traditional non-cloud model of computing is similar to everyone buying their own generators to create electricity for their own use.

_{source: https://commons.wikimedia.org}

Cloud computing can deliver computing services at three levels:

1. Infrastructure as a service (IaaS) delivers computer infrastructure as a service. For example, a user can deploy virtual servers on the cloud instead of buying physical hardware and installing server software on them. Another example would be a customer using storage space on the cloud for off-site storage of data. Rackspace is an example of an IaaS cloud provider. Amazon Elastic Compute Cloud (Amazon EC2) is another one.

2. Platform as a service (PaaS) provides a platform on which developers can build applications. Developers do not have to worry about infrastructure issues such as deploying servers or load balancing as is required when using IaaS. Those aspects are automatically taken care of by the platform. The price to pay is reduced flexibility; applications written on PaaS are limited to facilities provided by the platform. A PaaS example is the Google App Engine where developers can build applications using Java, Python, PHP, or Go whereas Amazon EC2 allows users to deploy applications written in any language on their virtual servers.

3. Software as a service (SaaS) allows applications to be accessed over the network instead of installing them on a local machine. For example, Google Docs is a SaaS word processing software, while Microsoft Word is a traditional word processing software.

Developer-to-developer documentation can be in one of two forms:

1. Documentation for developer-as-user: Software components are written by developers and reused by other developers, which means there is a need to document how such components are to be used. Such documentation can take several forms:
   • API documentation: APIs expose functionality in small-sized, independent and easy-to-use chunks, each of which can be documented systematically.
   • Tutorial-style instructional documentation: In addition to explaining functions/methods independently, some higher-level explanations of how to use an API can be useful.

2. Documentation for developer-as-maintainer: There is a need to document how a system or a component is designed, implemented and tested so that other developers can maintain and evolve the code. Writing documentation of this type is harder because of the need to explain complex internal details. However, given that readers of this type of documentation usually have access to the source code itself, only some information needs to be included in the documentation, as code (and code comments) can also serve as a complementary source of information.

Another view proposed by Daniele Procida in this article is as follows:

There is a secret that needs to be understood in order to write good software documentation: there isn’t one thing called documentation, there are four. They are: tutorials, how-to guides, explanation and technical reference. They represent four different purposes or functions, and require four different approaches to their creation. Understanding the implications of this will help improve most software documentation - often immensely. ...

TUTORIALS

A tutorial:

• is learning-oriented
• allows the newcomer to get started
• is a lesson

Analogy: teaching a small child how to cook

HOW-TO GUIDES

A how-to guide:

• is goal-oriented
• shows how to solve a specific problem
• is a series of steps

Analogy: a recipe in a cookery book

EXPLANATION

An explanation:

• is understanding-oriented
• explains
• provides background and context

Analogy: an article on culinary social history

REFERENCE

A reference guide:

• is information-oriented
• describes the machinery
• is accurate and complete

Analogy: a reference encyclopedia article

Software documentation (applies to both user-facing and developer-facing) is best kept in a text format for ease of version tracking. A writer-friendly source format is also desirable as non-programmers (e.g., technical writers) may need to author/edit such documents. As a result, formats such as Markdown, AsciiDoc, and PlantUML are often used for software documentation.

When writing project documents, a top-down breadth-first explanation is easier to understand than a bottom-up one.

The main advantage of the top-down approach is that the document is structured like an upside down tree (root at the top) and the reader can travel down a path she is interested in until she reaches the component she is interested to learn in-depth, without having to read the entire document or understand the whole system.

Technical documents exist to help others understand technical details. Therefore, it is not enough for the documentation to be accurate and comprehensive; it should also be comprehensible.

Here are some tips on writing effective documentation.

• Use plenty of diagrams: It is not enough to explain something in words; complement it with visual illustrations (e.g. a UML diagram).
• Use plenty of examples: When explaining algorithms, show a running example to illustrate each step of the algorithm, in parallel to worded explanations.
• Use simple and direct explanations: Convoluted explanations and fancy words will annoy readers. Avoid long sentences.
• Get rid of statements that do not add value: For example, 'We made sure our system works perfectly' (who didn't?), 'Component X has its own responsibilities' (of course it has!).
• It is not a good idea to have separate sections for each type of artifact, such as 'use cases', 'sequence diagrams', 'activity diagrams', etc. Such a structure, coupled with the indiscriminate inclusion of diagrams without justifying their need, indicates a failure to understand the purpose of documentation. Include diagrams when they are needed to explain something. If you want to provide additional diagrams for completeness' sake, include them in the appendix as a reference.

Aim for 'just enough' developer documentation.

• Writing and maintaining developer documents is an overhead. You should try to minimize that overhead.
• If the readers are developers who will eventually read the code, the documentation should complement the code and should provide only just enough guidance to get started.

Anything that is already clear in the code need not be described in words. Instead, focus on providing higher level information that is not readily visible in the code or comments.

Refrain from duplicating chunks of text. When describing several similar algorithms/designs/APIs, etc., do not simply duplicate large chunks of text. Instead, describe the similarities in one place and emphasize only the differences in other places. It is very annoying to see pages and pages of similar text without any indication as to how they differ from each other.

When testing, you execute a set of test cases. A test case specifies how to perform a test. At a minimum, it specifies the input to the software under test (SUT) and the expected behavior.

Test cases can be determined based on the specification, reviewing similar existing systems, or comparing to the past behavior of the SUT.

For each test case you should do the following:

1. Feed the input to the SUT
2. Observe the actual output
3. Compare actual output with the expected output

A test case failure is a mismatch between the expected behavior and the actual behavior. A failure indicates a potential defect (or a bug), unless the error is in the test case itself.

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.

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

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

    

    


    
    

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.

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.

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.

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.

A simple way to semi-automate testing of a CLI (Command Line Interface) app is by using input/output re-direction.

• First, you feed the app with a sequence of test inputs that is stored in a file while redirecting the output to another file.
• Next, you compare the actual output file with another file containing the expected output.

Let's assume you are testing a CLI app called AddressBook. Here are the detailed steps:

1. Store the test input in the text file input.txt.

   Example input.txt

   ---

2. Store the output you expect from the SUT in another text file expected.txt.

   Example expected.txt

   ---

3. Run the program as given below, which will redirect the text in input.txt as the input to AddressBook and similarly, will redirect the output of AddressBook to a text file output.txt. Note that this does not require any code changes to AddressBook.

   java AddressBook < input.txt > output.txt
   
       
   
       
   
   
       
       

   • The way to run a CLI program differs based on the language.
     e.g., In Python, assuming the code is in AddressBook.py file, use the command
     python AddressBook.py < input.txt > output.txt

   • If you are using Windows, use a normal command window to run the app, not a PowerShell window.

   More on the > operator and the < operator extra

4. Next, you compare output.txt with the expected.txt. This can be done using a utility such as Windows' FC (i.e. File Compare) command, Unix's diff command, or a GUI tool such as WinMerge.

   FC output.txt expected.txt
   
       
   
       
   
   
       
       

Note that the above technique is only suitable when testing CLI apps, and only if the exact output can be predetermined. If the output varies from one run to the other (e.g. it contains a time stamp), this technique will not work. In those cases, you need more sophisticated ways of automating tests.

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

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

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.

Consider the testing of the following operation.

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.

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.

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

An EP may not have adjacent values.

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.

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.

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).

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.

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.

Software Quality Assurance (QA) is the process of ensuring that the software being built has the required levels of quality.

While testing is the most common activity used in QA, there are other complementary techniques such as static analysis, code reviews, and formal verification.

Code review is the systematic examination of code with the intention of finding where the code can be improved.

Reviews can be done in various forms. Some examples below:

• Pull Request reviews

  • Project Management Platforms such as GitHub and BitBucket allow the new code to be proposed as Pull Requests and provide the ability for others to review the code in the PR.

• In pair programming

  • As pair programming involves two programmers working on the same code at the same time, there is an implicit review of the code by the other member of the pair.

• Formal inspections

  • Inspections involve a group of people systematically examining project artifacts to discover defects. Members of the inspection team play various roles during the process, such as:

    • the author - the creator of the artifact
    • the moderator - the planner and executor of the inspection meeting
    • the secretary - the recorder of the findings of the inspection
    • the inspector/reviewer - the one who inspects/reviews the artifact

Advantages of code review over testing:

• It can detect functionality defects as well as other problems such as coding standard violations.
• It can verify non-code artifacts and incomplete code.
• It does not require test drivers or stubs.

Disadvantages:

• It is a manual process and therefore, error prone.

Static analysis of code can find useful information such as unused variables, unhandled exceptions, style errors, and statistics. Most modern IDEs come with some inbuilt static analysis capabilities. For example, an IDE can highlight unused variables as you type the code into the editor.

The term static in static analysis refers to the fact that the code is analyzed without executing the code. In contrast, dynamic analysis requires the code to be executed to gather additional information about the code e.g., performance characteristics.

Higher-end static analysis tools (static analyzers) can perform more complex analysis such as locating potential bugs, memory leaks, inefficient code structures, etc.

Linters are a subset of static analyzers that specifically aim to locate areas where the code can be made 'cleaner'.

Formal verification uses mathematical techniques to prove the correctness of a program.

Advantages:

• Formal verification can be used to prove the absence of errors. In contrast, testing can only prove the presence of errors, not their absence.

Disadvantages:

• It only proves the compliance with the specification, but not the actual utility of the software.
• It requires highly specialized notations and knowledge which makes it an expensive technique to administer. Therefore, formal verifications are more commonly used in safety-critical software such as flight control systems.

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 milestone is the end of a stage which indicates significant progress. You 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, you can use a high-level plan for the whole project and a detailed plan for the next few milestones.

A buffer is 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 are 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 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 keep track of tasks using simple tools such as online spreadsheets or general-purpose/light-weight task 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):

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 experts, network technology experts, etc. This allows individual group members to concentrate solely on the areas in which they have sound knowledge and expertise.

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

Strict hierarchy team

At 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 a bureaucratic government. Each team member only works on his/her 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.

Software development goes through different stages such as requirements, analysis, design, implementation and testing. These stages are collectively known as the software development life cycle (SDLC). There are several approaches, known as software development life cycle models (also called software process models), that describe different ways to go through the SDLC. Each process model prescribes a "roadmap" for the software developers to manage the development effort. The roadmap describes the aims of the development stage(s), the artifacts or outcome of each stage, as well as the workflow i.e. the relationship between stages.

The sequential model, also called the waterfall model, models software development as a linear process, in which the project is seen as progressing steadily in one direction through the development stages. The name waterfall stems from how the model is drawn to look like a waterfall (see below).

When one stage of the process is completed, it should produce some artifacts to be used in the next stage. For example, upon completion of the requirements stage, a comprehensive list of requirements is produced that will see no further modifications. A strict application of the sequential model would require each stage to be completed before starting the next.

This could be a useful model when the problem statement is well-understood and stable. In such cases, using the sequential model should result in a timely and systematic development effort, provided that all goes well. As each stage has a well-defined outcome, the progress of the project can be tracked with relative ease.

The major problem with this model is that the requirements of a real-world project are rarely well-understood at the beginning and keep changing over time. One reason for this is that users are generally not aware of how a software application can be used without prior experience in using a similar application.

The iterative model (sometimes called iterative and incremental) advocates having several iterations of SDLC. Each of the iterations could potentially go through all the development stages, from requirements gathering to testing & deployment. Roughly, it appears to be similar to several cycles of the sequential model.

In this model, each of the iterations produces a new version of the product. Feedback on the new version can then be fed to the next iteration. Taking the Minesweeper game as an example, the iterative model will deliver a fully playable version from the early iterations. However, the first iteration will have primitive functionality, for example, a clumsy text based UI, fixed board size, limited randomization, etc. These functionalities will then be improved in later releases.

The iterative model can take a breadth-first or a depth-first approach to iteration planning.

• breadth-first: an iteration evolves all major components in parallel e.g., add a new feature fully, or enhance an existing feature.
• depth-first: an iteration focuses on fleshing out only some components e.g., update the backend to support a new feature that will be added in a future iteration.

Most projects use a mixture of breadth-first and depth-first iterations i.e., an iteration can contain some breadth-first work as well as some depth-first work.

In 2001, a group of prominent software engineering practitioners met and brainstormed for an alternative to documentation-driven, heavyweight software development processes that were used in most large projects at the time. This resulted in something called the agile manifesto (a vision statement of what they were looking to do).

You are uncovering better ways of developing software by doing it and helping others do it.

Through this work you have come to value:

• Individuals and interactions over processes and tools
• Working software over comprehensive documentation
• Customer collaboration over contract negotiation
• Responding to change over following a plan

That is, while there is value in the items on the right, you value the items on the left more.
_{-- Extract from the Agile Manifesto}

Subsequently, some of the signatories of the manifesto went on to create process models that try to follow it. These processes are collectively called agile processes. Some of the key features of agile approaches are:

• Requirements are prioritized based on the needs of the user, are clarified regularly (at times almost on a daily basis) with the entire project team, and are factored into the development schedule as appropriate.
• Instead of doing a very elaborate and detailed design and a project plan for the whole project, the team works based on a rough project plan and a high level design that evolves as the project goes on.
• There is a strong emphasis on complete transparency and responsibility sharing among the team members. The team is responsible together for the delivery of the product. Team members are accountable, and regularly and openly share progress with each other and with the user.

There are a number of agile processes in the development world today. eXtreme Programming (XP) and Scrum are two of the well-known ones.

The following description was adapted from the XP home page, emphasis added:

Extreme Programming (XP) stresses customer satisfaction. Instead of delivering everything you could possibly want on some date far in the future, this process delivers the software you need as you need it.

XP aims to empower developers to confidently respond to changing customer requirements, even late in the life cycle.

XP emphasizes teamwork. Managers, customers, and developers are all equal partners in a collaborative team. XP implements a simple, yet effective environment enabling teams to become highly productive. The team self-organizes around the problem to solve it as efficiently as possible.

XP aims to improve a software project in five essential ways: communication, simplicity, feedback, respect, and courage. Extreme Programmers constantly communicate with their customers and fellow programmers. They keep their design simple and clean. They get feedback by testing their software starting on day one. Every small success deepens their respect for the unique contributions of each and every team member. With this foundation, Extreme Programmers are able to courageously respond to changing requirements and technology.

XP has a set of simple rules. XP is a lot like a jig saw puzzle with many small pieces. Individually the pieces make no sense, but when combined together a complete picture can be seen. This flow chart shows how Extreme Programming's rules work together.

Pair programming, CRC cards, project velocity, and standup meetings are some interesting topics related to XP. Refer to extremeprogramming.org to find out more about XP.