Introduction

Our second-year Java assignment, the calculator project, was more about using a working calculator application than the actual application. It focused on proper procedures concerning version control, test-driven development, documentation, and quality assurance based on linting and styling. Thus, an overall approach to the course will enable us to appreciate the fact that software development is much more than just writing the working code—it is about making maintainable, understandable, and scalable software.

Version Control Systems: A Lifeline of Software Engineering

Version control systems represent the majority of unsung heroes in software engineering. Throughout the scope of our assignment, we learned about SVN—a Distributed VCS that allows teams to work concurrently on a codebase, merge changes, and even revert to previous states. Our use of SVN has now introduced us to the concepts of branching, tags, releases, code history, and deltas.

Branching: It allows developers to work on copies of the code base in parallel. The major essence of the branching model is creating isolation from the main code base to be able to implement features, fix bugs, or even try something new without tampering with the main code base. Branching thus allows parallel development of features that are independent of each other. Once this feature is complete, it can now be merged into the main branch. This ensures an easier and more organized workflow.
Tags: These are mainly useful for labeling particular versions of your code. This technique is commonly used to create stable releases or at milestones in your project. This places 'bookmarks' in your history that you can easily jump between in order to examine or roll back to a particular version associated with a certain milestone or release.
Releases: These are stable versions of the software that are ready to be deployed. These versions have undergone intense testing and are ready for shipping out to the end user. VCS management of releases ensures that only rigorously vetted code reaches the end user, improving quality and reliability in its software.
Code history: This lets developers trace the change over time. In such a manner, it is easier to get an understanding of how the codebase was modified and determine when a certain change was added. From code history, one can learn useful lessons in the development process of identification and analysis of patterns or trends in the codebase.
Deltas: Represent differences between two file versions, showing what was added, modified, or deleted. It gives a very fine-grained look at the changes, which is good for developers to review and understand the impact of modifications. This insight comes in particularly handy while debugging any problems or trying to realize the effect of some change.

In our experience with SVN in the calculator project, all these features proved their benefits in practice when using a VCS:

Reverting Changes: VCS allowed us to revert to any past state of the code. If a change recently added a bug, or if we just needed to visit an earlier state of the project, we did it easily. This provided a safety net that gave us the freedom to explore and experiment without fear of irreversible consequences.
Backup and Redundancy: Since the code was on a remote server, VCS also serves as a backup of sorts. If the hardware failed or the data was irrevocably lost through some other means on a local machine, then it could be restored without a hassle from the remote repository.
Collaboration: We were better able to collaborate with one another because VCS made this easy. With the availability of branching, different developers were able to work on independent features all at once without impeding each other's progress. The ability to merge had eventually enabled us to stitch our individual efforts into one coherent whole.

In other words, a VCS is source control management that allows versioning of the code and a fully functioning system supporting collaboration, experimentation, and robust and reliable development of software. Our calculator project put a premium on the place VCS occupies in the software engineering process by suggesting that, yes, it is a sort of a lifeline during development.

Unit Testing and Test-Driven Development

It was the first project we had undertaken using unit testing and test-driven development. For tests we used JUnit; the concept was that we would write tests first before writing the implementation. That was pretty straightforward: you define the expected behavior in the form of tests, then write code to pass it.

Unit tests basically provide a kind of documentation in their own right. One can understand from the tests what type of behavior is expected from the codebase, how different components are supposed to interact, and what kind of output is expected to be produced when it receives some type of input.

TDD creates a codebase of robustness and reliability. First, in the writing of tests, we ensure that the code that will be written is testable, modular, and has clear specifications. This will result in fewer bugs, and developers can make changes with confidence, knowing that any improvement will not introduce any regression that will be caught by the tests.

Better yet, unit testing is a good friend when it comes to refactoring. Refactoring, on the other hand, refers to the restructuring of existing code without changing its external behavior: in other words, its primary purpose is to improve nonfunctional attributes of the software, making it easier to understand, less complex, and increasing its maintainability. As much as restructuring is useful, it can be treacherous without a good set of tests in place, since it is easy to introduce bugs.

And unit tests decrease that risk. Suppose you have a suite of unit tests – they cover most of your code. Then you can refactor with a lot more confidence. Run the tests after making changes. If they all pass, you can be reasonably sure that your changes didn't break anything. If the tests fail, the test that caused the error can point out where a problem was introduced. That tight feedback loop will make the process of refactoring faster and safer.

When there's a full set of unit tests, this can also motivate more frequent refactoring of the code, eventually leading to a cleaner and more understandable code base. This will allow easier working of the code, minimize the chances of bugs appearing, and potentially even make adding new features quicker in the long run.

The refactoring process supported by unit testing during our calculator project not only let us confirm the working of the code in expected ways but also allowed us to retain a clean, efficient, and maintainable codebase. Flexibility, as defined by unit testing in coming up with a strong and functional calculator application, turned out to be quite invaluable.

Documentation: Moving Through the Codebase

Code documentation is like the map of a codebase. In the calculator project, we did JavaDoc for the classes, methods, and their parameters. There are several ways documentation is helpful:

Understand the code: We know what a piece of code does and why it is implemented so.
Onboarding new developers: New team members come and get up to speed by reading the documentation.
Maintain the code: You make modifications confidently if you know the ins and outs of the code.

One needs documentation not just for others but for our sake, too. We might forget what really is there in the code.

Code Quality: More than Just Functionality

It touches upon the fact that code quality is more than just getting a program to run but about writing readable, maintainable, and consistent code. Generally, code quality matters in the long run for the success of the project. This is because it affects how the system will be easily readable, updatable, and debuggable. We have considered several quality features associated with our calculator project.

Design Patterns are solutions to problems of software design and are formulated templates ready for specific situations. Design patterns realize more efficient, more scalable, and more maintainable code by using them. Some common design patterns include, among others :
Singleton Pattern: Ensures that at any time, there is only one instance of a class with global access coordinates to it. This is especially useful when one shared resource, such as a configuration object, is needed.
Observer Pattern: A design pattern where one object (named "subject") can publish changes to its state, letting objects that are "observers" react. That is a general pattern used in event handling.
Factory Pattern: It provides an interface for creating objects without specifying the exact class of object it will create but allowing its subclasses to alter the type of objects that will be created. For instance, it's most used when it's required to create objects but other classes define which object should be created.
Strategy Pattern: The Strategy pattern defines a family of algorithms, encapsulates each one, and makes them interchangeable. It lets the algorithm vary independently of the client using it.

While using these and other design patterns in our calculator project, we can write more organized, maintainable code to gain a better understanding of it ourselves and other developers for future code changes or extensions.

Code Linting and Styling are as essential to quality code as any other aspect. Checkstyle was used to check Java code on a defined set of rules. Style brings about uniformity to the codebase; hence, following the same style made our code very readable and easy to work on by us and any other developer.

It improves code readability and eventually assists in finding variables declared but not in use, undeclared, or has mismatched types. By catching these issues early, we were able to reduce the number of bugs in our code and improve its overall quality.

In the final analysis, quality of your code has come to mean more than just functionality. It also equals to readability, maintainability, and consistency. This brings us to a second dimension of code quality—stuff that's going to make the code not just functional but robust, scalable, and easy to read. We shall ensure this in our calculator project using design patterns and usage of linting tools appropriately, with a consistent code style of writing the code.

Conclusion

The calculator project was so much more than a Java assignment; in effect, it was a learning experience on software engineering methodologies. It made us understand that successful software development is a direct derivative of proper version control, test-driven development, comprehensive documentation, and high-quality code. It is not only the destination which matters but also the journey itself in making the software maintainable, comprehensible, and scalable. These are lessons that will be the guiding philosophy in each of our further steps concerning software development.