Backend Services Optimization

Building backend behavior before building an interface.

This project was developed as part of CS 320 and placed me in the role of a software engineer working for the fictional company Grand Strand Systems.

The assignment focused entirely on backend application behavior. Rather than building a user interface, I created service classes responsible for managing three application domains: contacts, tasks, and appointments.

Each domain had its own object representation, service manager, validation constraints, and JUnit tests. This made the project an exercise in both software construction and quality assurance.

Separating data models from service behavior.

The application was organized around independent domain services. Each domain represented its own data and exposed operations through a corresponding service class.

Keeping these responsibilities independent meant changes to one service could be reasoned about without mixing unrelated contact, task, or appointment behavior.

Managing contact data through controlled service operations.

The Contact service provides backend operations for creating, retrieving, updating, and deleting contact records.

The service also needed to account for conditions such as duplicate identifiers and invalid or null field values instead of allowing incorrect data to enter the system.

Enforcing constraints around task data.

The Task service follows the same modular approach while managing a different set of application requirements.

Task records include identifying information, a task name, and a description. The name and description are validated against defined character limits so that invalid objects cannot silently become part of the application state.

Applying validation to scheduling behavior.

The Appointment service manages scheduling information through its own domain model and service operations.

Appointment data introduces constraints that differ from contacts and tasks because dates and scheduling rules must also be considered. This required validation around the state of appointment objects before the service accepted them.

Preventing invalid state before it spreads.

Validation was a central part of the project. The objective was not only to test whether valid objects worked, but also to make sure invalid data was rejected consistently.

Testing behavior from both directions.

I created JUnit tests for the individual service classes and their object constraints. The suite included both positive and negative test cases.

This distinction matters because a test suite that only confirms successful behavior says very little about what happens when the application receives bad data.

Verifying that service operations produce retrievable state.

One basic unit-test pattern creates a service, adds a valid domain object, retrieves that object again, and verifies that the expected data was preserved.

@Test
public void testAddContact() {
    ContactService service = new ContactService();

    Contact contact = new Contact(
        "123",
        "Takeria",
        "5551234567",
        "Galaxy Blvd"
    );

    service.addContact(contact);

    assertEquals(
        "Takeria",
        service.getContact("123").getFirstName()
    );
}

A test like this verifies more than object construction. It checks the interaction between the domain object and the service responsible for storing and retrieving it.

Using coverage as a signal—not as the entire definition of quality.

The test suite exceeded 80% coverage, giving broad automated verification across the backend components.

Coverage helped reveal whether important code paths had been exercised, but the more meaningful goal was ensuring that tests represented application requirements and edge conditions.

A line of code being executed during a test does not automatically prove that its behavior is correct. This project reinforced the difference between measuring test activity and designing meaningful test cases.

Applying object-oriented boundaries to backend responsibilities.

The services were structured using object-oriented design principles. Each domain had a focused model and service instead of placing all application behavior into one large class.

Designing services that are easier to change safely.

Modular service design made the code easier to test and reason about because each component had a limited responsibility.

This structure also creates a cleaner path for future additions such as persistence, APIs, or a user interface because the core application behavior already exists independently.

Treating testing as part of development rather than a final check.

Unit testing exposed edge cases that were easy to overlook when thinking only about the expected flow of the application.

Null values, duplicated identifiers, length limits, and other constraints forced the implementation to define exactly what valid application state should look like.

Automated testing then created a repeatable way to verify those decisions whenever the implementation changed.

Learning to build software that can prove its own behavior.

The most important lesson from this project was that backend quality depends on more than implementing the happy path.

Writing unit tests made requirements more concrete. Instead of saying that an identifier should be unique or that a field should reject an invalid value, the test suite provided an executable demonstration of that rule.

The project also reinforced how modularity supports testing. Smaller, focused service classes are easier to instantiate, isolate, test, and reason about than systems where responsibilities are tightly mixed.

Extending beyond isolated unit behavior.

This project focused primarily on unit testing because its backend services were deliberately isolated from a user interface or larger production system.

A natural next stage would be to add higher levels of testing as the architecture grows.

A tested foundation for reliable backend development.

The final project demonstrated how backend service design and automated testing support each other. The services provided focused application behavior while the JUnit suite verified that those behaviors remained within their defined constraints.

The project gave me a stronger foundation in writing backend software that is not only functional, but structured so that its behavior can be verified, maintained, and extended with confidence.