Week 8 [Fri, Oct 2nd] - Topics

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.

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

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

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

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

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

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

Some examples of different coupling types:

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

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

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

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

Cohesion can be present in many forms. Some examples:

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

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

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

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

Can identify n-tier architectural style

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

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

This principle should lead to higher cohesion and lower coupling.

Can explain coupling

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

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

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

Discuss the coupling levels of alternative designs x and y.

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

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

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

Discuss the relationship between coupling and a measure of how easily a given component can be testedtestability.

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

Choose the correct statements.

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

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

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

If a class has only one responsibility, it needs to change only when there is a change to that responsibility.

Gather together the things that change for the same reasons. Separate those things that change for different reasons. _{―- Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin}

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):

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

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

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

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

A sample Gantt chart:

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

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

• Nodes or vertices capture the effort estimations of tasks, and
• Arrows depict the precedence between tasks

A PERT chart can help determine the following important information:

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

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

Egoless team

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

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

Chief programmer team

Frederick Brooks proposed that software engineers learn from the medical surgical team in an operating room. In such a team, there is always a chief surgeon, assisted by experts in other areas. Similarly, in a chief programmer team structure, there is a single authoritative figure, the chief programmer. Major decisions, e.g. system architecture, are made solely by him/her and obeyed by all other team members. The chief programmer directs and coordinates the effort of other team members. When necessary, the chief will be assisted by domain specialists e.g. business specialists, database 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 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.

Quality Assurance = Validation + Verification

QA involves checking two aspects:

1. Validation: are you building the right system i.e., are the requirements correct?
2. Verification: are you building the system right i.e., are the requirements implemented correctly?

Whether something belongs under validation or verification is not that important. What is more important is that both are done, instead of limiting to only verification (i.e., remember that the requirements can be wrong too).

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.

Testability is an indication of how easy it is to test an SUT. As testability depends a lot on the design and implementation, you should try to increase the testability when you design and implement software. The higher the testability, the easier it is to achieve better quality software.

Skim through the JUnit 5 User Guide to see what advanced techniques are available. If applicable, feel free to adopt them.