Context

Most applications support storage/retrieval of information, displaying of information to the user (often via multiple UIs having different formats), and changing stored information based on external inputs.

Problem

The high coupling that can result from the interlinked nature of the features described above.

Solution

Decouple data, presentation, and control logic of an application by separating them into three different components: Model, View and Controller.

• View: Displays data, interacts with the user, and pulls data from the model if necessary.
• Controller: Detects UI events such as mouse clicks, button pushes and takes follow up action. Updates/changes the model/view when necessary.
• Model: Stores and maintains data. Updates views if necessary.

The relationship between the components can be observed in the diagram below. Typically, the UI is the combination of view and controller.

Given below is a concrete example of MVC applied to a student management system. In this scenario, the user is retrieving data of one student.

In the diagram above, when the user clicks on a button using the UI, the ‘click’ event is caught and handled by the UiController. The ref frame indicates that the interactions within that frame have been extracted out to another separate sequence diagram.

Note that in a simple UI where there’s only one view, Controller and View can be combined as one class.

There are many variations of the MVC model used in different domains. For example, the one used in a desktop GUI could be different from the one used in a Web application.

Context

An object (possibly, more than one) is interested to get notified when a change happens to another object. That is, some objects want to ‘observe’ another object.

Consider this scenario from the a student management system where the user is adding a new student to the system.

Now, assume the system has two additional views used in parallel by different users:

• StudentListUi: that accesses a list of students and
• StudentStatsUi: that generates statistics of current students.

When a student is added to the database using NewStudentUi shown above, both StudentListUi and StudentStatsUi should get updated automatically, as shown below.

However, the StudentList object has no knowledge about StudentListUi and StudentStatsUi (note the direction of the navigability) and has no way to inform those objects. This is an example of the type of problem addressed by the Observer pattern.

Problem

The ‘observed’ object does not want to be coupled to objects that are ‘observing’ it.

Solution

Force the communication through an interface known to both parties.

Here is the generic description of the observer pattern:

• <<Observer>> is an interface: any class that implements it can observe an <<Observable>>. Any number of <<Observer>> objects can observe (i.e. listen to changes of) the <<Observable>> object.
• The <<Observable>> maintains a list of <<Observer>> objects. addObserver(Observer) operation adds a new <<Observer>> to the list of <<Observer>>s.
• Whenever there is a change in the <<Observable>>, the notifyObservers() operation is called that will call the update() operation of all <<Observer>>s in the list.

The most famous source of design patterns is the "Gang of Four" (GoF) book which contains 23 design patterns divided into three categories:

• Creational: About object creation. They separate the operation of an application from how its objects are created.
  • Abstract Factory, Builder, Factory Method, Prototype, Singleton
• Structural: About the composition of objects into larger structures while catering for future extension in structure.
  • Adapter, Bridge, Composite, Decorator, Façade, Flyweight, Proxy
• Behavioral: Defining how objects interact and how responsibility is distributed among them.
  • Chain of Responsibility, Command, Interpreter, Template Method, Iterator, Mediator, Memento, Observer, State, Strategy, Visitor

Design patterns are usually embedded in a larger design and sometimes applied in combination with other design patterns.

Let us look at a case study that shows how design patterns are used in the design of a class structure for a Stock Inventory System (SIS) for a shop. The shop sells appliances, and accessories for the appliances. SIS simply stores information about each item in the store.

Use Cases:

• Create a new item
• View information about an item
• Modify information about an item
• View all available accessories for a given appliance
• List all items in the store

SIS can be accessed using multiple terminals. Shop assistants use their own terminals to access SIS, while the shop manager’s terminal continuously displays a list of all items in store. In the future, it is expected that suppliers of items use their own applications to connect to SIS to get real-time information about current stock status. User authentication is not required for the current version, but may be required in the future.

A step by step explanation of the design is given below. Note that this is one out of many possible designs. Design patterns are also applied where appropriate.

A StockItem can be an Appliance or an Accessory.

To track that each Accessory is associated with the correct Appliance, consider the following alternative class structures.

The third one seems more appropriate (the second one is suitable if accessories can have accessories). Next, consider between keeping a list of Appliances, and a list of StockItems. Which is more appropriate?

The latter seems more suitable because it can handle both appliances and accessories the same way. Next, an abstraction occurrence pattern is applied to keep track of StockItems.

Note the inclusion of navigabilities. Here’s a sample object diagram based on the class model created thus far.

Next, apply the façade pattern to shield the SIS internals from the UI.

As UI consists of multiple views, the MVC pattern is applied here.

Some views need to be updated when the data change; apply the Observer pattern here.

In addition, the Singleton pattern can be applied to the façade class.

Design pattern provides a high-level vocabulary to talk about design.

Knowing more patterns is a way to become more ‘experienced’. Aim to learn at least the context and the problem of patterns  so that when you encounter those problems you know where to look for a solution.

Some patterns are domain-specific  e.g. patterns for distributed applications, some are created in-house  e.g. patterns in the company/project and some can be self-created  e.g. from past experience.

Be careful not to overuse patterns. Do not throw patterns at a problem at every opportunity. Patterns come with overhead such as adding more classes or increasing the levels of abstraction. Use them only when they are needed. Before applying a pattern, make sure that:

• there is substantial improvement in the design, not just superficial.
• the associated tradeoffs are carefully considered. There are times when a design pattern is not appropriate (or an overkill).

Design principles have varying degrees of formality – rules, opinions, rules of thumb, observations, and axioms. Compared to design patterns, principles are more general, have wider applicability, with correspondingly greater overlap among them.

The notion of capturing design ideas as "patterns" is usually attributed to Christopher Alexander. He is a building architect noted for his theories about design. His book Timeless way of building talks about "design patterns" for constructing buildings.

Here is a sample pattern from that book:

When a room has a window with a view, the window becomes a focal point: people are attracted to the window and want to look through it. The furniture in the room creates a second focal point: everyone is attracted toward whatever point the furniture aims them at (usually the center of the room or a TV). This makes people feel uncomfortable. They want to look out the window, and toward the other focus at the same time. If you rearrange the furniture, so that its focal point becomes the window, then everyone will suddenly notice that the room is much more “comfortable”

Apparently, patterns and anti-patterns are found in the field of building architecture. This is because they are general concepts applicable to any domain, not just software design. In software engineering, there are many general types of patterns: Analysis patterns, Design patterns, Testing patterns, Architectural patterns, Project management patterns, and so on.

In fact, the abstraction occurrence pattern is more of an analysis pattern than a design pattern, while MVC is more of an architectural pattern.

New patterns can be created too. If a common problem needs to be solved frequently that leads to a non-obvious and better solution, it can be formulated as a pattern so that it can be reused by others. However, don’t reinvent the wheel; the pattern might already exist.

A deployment diagram shows a system's physical layout, revealing which pieces of software run on which pieces of hardware.

An example deployment diagram:

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

A component diagram is used to show how a system is divided into components and how they are connected to each other through interfaces.

A package diagram shows packages and their dependencies. A package is a grouping construct for grouping UML elements (classes, use cases, etc.).

A composite structure diagram hierarchically decomposes a class into its internal structure.

Here is an example composite structure diagram:

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

A timing diagram focus on timing constraints.

Here is an example timing diagram:

_{Adapted from: UML Distilled by Martin Fowler}

An Interaction overview diagrams is a combination of activity diagrams and sequence diagrams.

An example:

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

A Communication diagrams are like sequence diagrams but emphasize the data links between the various participants in the interaction rather than the sequence of interactions.

An example:

_{Adapted from: UML Distilled by Martin Fowler}

A State Machine Diagram models state-dependent behavior.

Often, state-dependent behavior displayed by an object in a system is simple enough that it needs no extra attention; such a behavior can be as simple as a conditional behavior like if x>y, then x=x-y. Occasionally, objects may exhibit state-dependent behavior that is complex enough such that it needs to be captured into a separate model. Such state-dependent behavior can be modelled using UML state machine diagrams (SMD for short, sometimes also called ‘state charts’, ‘state diagrams’ or ‘state machines’).

An SMD views the life-cycle of an object as consisting of a finite number of states where each state displays a unique behavior pattern. An SMD captures information such as the states an object can be in, during its lifetime, and how the object responds to various events while in each state and how the object transits from one state to another. In contrast to sequence diagrams that capture object behavior one scenario at a time, SMDs capture the object’s behavior over its full life cycle.

An SMD for the Minesweeper game.

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

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

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

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

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

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

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

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

There are other strategies that can be used too.

Consider the following scenario.

Suppose these are the test cases being considered.

It looks like the test cases were created using the at least once strategy. After running these tests can we confirm that square-format label printing is done correctly?

• Answer: No.
• Reason: Cherry -- the only input that can produce a square-format label -- is in a negative test case which produces an error message instead of a label. If there is a bug in the code that prints labels in square-format, these tests cases will not trigger that bug.

In this case a useful heuristic to apply is each valid input must appear at least once in a positive test case. Cherry is a valid test input and we must ensure that it appears at least once in a positive test case. Here are the updated test cases after applying that heuristic.

Consider the test cases designed in [Heuristic: each valid input at least once in a positive test case].

After running these test cases can you be sure that the error message “invalid price” is shown for negative prices?

• Answer: No.
• Reason: -1 -- the only input that is a negative price -– is in a test case that produces the error message “invalid fruit”.

In this case a useful heuristic to apply is no more than one invalid input in a test case. After applying that, we get the following test cases.

Consider the calculateGrade scenario given below:

To get the first cut of test cases, let’s apply the at least once strategy.

Next, let’s apply the each valid input at least once in a positive test case heuristic. Test case 5 has a valid value for projectGrade=F that doesn't appear in any other positive test case. Let's replace test case 5 with 5.1 and 5.2 to rectify that.

Next, we apply the no more than one invalid input in a test case heuristic. Test cases 5.2 and 6 don't follow that heuristic. Let's rectify the situation as follows:

Next, let us assume that there is a dependency between the inputs examScore and isAbsent such that an absent student can only have examScore=0. To cater for the hidden invalid case arising from this, we can add a new test case where isAbsent=true and examScore!=0. In addition, test cases 3-6.2 should have isAbsent=false so that the input remains valid.

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 requirement 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 that 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 a relative ease.

The major problem with this model is that 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 requirement 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 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.
• depth-first: an iteration focuses on fleshing out only some components.

Most project use a mixture of breadth-first and depth-first iterations. Hence, the common phrase ‘an iterative and incremental process’.

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).

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

Through this work we 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, we 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.
• 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.

This description of Scrum was adapted from Wikipedia [retrieved on 18/10/2011], emphasis added:

Scrum is a process skeleton that contains sets of practices and predefined roles. The main roles in Scrum are:

• The Scrum Master, who maintains the processes (typically in lieu of a project manager)
• The Product Owner, who represents the stakeholders and the business
• The Team, a cross-functional group who do the actual analysis, design, implementation, testing, etc.

A Scrum project is divided into iterations called Sprints. A sprint is the basic unit of development in Scrum. Sprints tend to last between one week and one month, and are a timeboxed (i.e. restricted to a specific duration) effort of a constant length.

Each sprint is preceded by a planning meeting, where the tasks for the sprint are identified and an estimated commitment for the sprint goal is made, and followed by a review or retrospective meeting, where the progress is reviewed and lessons for the next sprint are identified.

During each sprint, the team creates a potentially deliverable product increment (for example, working and tested software). The set of features that go into a sprint come from the product backlog, which is a prioritized set of high level requirements of work to be done. Which backlog items go into the sprint is determined during the sprint planning meeting. During this meeting, the Product Owner informs the team of the items in the product backlog that he or she wants completed. The team then determines how much of this they can commit to complete during the next sprint, and records this in the sprint backlog. During a sprint, no one is allowed to change the sprint backlog, which means that the requirements are frozen for that sprint. Development is timeboxed such that the sprint must end on time; if requirements are not completed for any reason they are left out and returned to the product backlog. After a sprint is completed, the team demonstrates the use of the software.

Scrum enables the creation of self-organizing teams by encouraging co-location of all team members, and verbal communication between all team members and disciplines in the project.

A key principle of Scrum is its recognition that during a project the customers can change their minds about what they want and need (often called requirements churn), and that unpredicted challenges cannot be easily addressed in a traditional predictive or planned manner. As such, Scrum adopts an empirical approach—accepting that the problem cannot be fully understood or defined, focusing instead on maximizing the team’s ability to deliver quickly and respond to emerging requirements.

Daily Scrum is another key scrum practice. The description below was adapted from https://www.mountaingoatsoftware.com (emphasis added):

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.

The unified process is developed by the Three Amigos - Ivar Jacobson, Grady Booch and James Rumbaugh (the creators of UML).

The unified process consists of four phases: inception, elaboration, construction and transition. The main purpose of each phase can be summarized as follows:

Given above is a visualization of a project done using the Unified process (source: Wikipedia). As the diagram shows, a phase can consist of several iterations. Each vertical column (labeled “I1” “E1”, “E2”, “C1”, etc.) represents a single iteration. Each of the iterations consists of a set of ‘workflows’ such as ‘Business modeling’, ‘Requirements’, ‘Analysis & Design’ etc. The shaded region indicates the amount of resource and effort spent on a particular workflow in a particular iteration.

Unified process is a flexible and customizable process model framework rather than a single fixed process. For example, the number of iterations in each phase, definition of workflows, and the intensity of a given workflow in a given iteration can be adjusted according to the nature of the project. Take the Construction Phase, to develop a simple system, one or two iterations would be sufficient. For a more complicated system, multiple iterations will be more helpful. Therefore, the diagram above simply records a particular application of the UP rather than prescribe how the UP is to be applied. However, this record can be refined and reused for similar future projects.

CMMI defines five maturity levels for a process and provides criteria to determine if the process of an organization is at a certain maturity level. The diagram below [taken from Wikipedia] gives an overview of the five levels.

This section has some exercise that cover multiple topics related to SDLC process models.