Testing, Deployment and Full-Stack Capstone

Test at the level of the behavior

Unit tests check focused logic quickly, while integration tests verify that components work together. Use JUnit for Java tests and Mockito where a collaborator needs to be isolated. For an API workflow, check the request, response status and important persisted result rather than asserting only that a method was called.

Tests should cover ordinary success, invalid input, missing records and permission boundaries where relevant. Avoid brittle tests that depend on internal implementation details. When a bug is fixed, add a test that would have failed before the fix.

Prepare a repeatable build

Maven manages dependencies and build tasks for the Java application. A clean build should be possible from documented steps. Keep environment-specific values outside the repository and provide safe example configuration names without real credentials. Database schema changes should be repeatable and tracked through a migration approach.

Git helps organize changes and collaborate through small, reviewable commits. Use branches or an agreed team workflow, inspect diffs before sharing, and avoid committing generated files or secrets. A code review should focus on correctness, readability, security and test coverage.

Think through deployment and operations

Deployment requires more than copying code: the application needs a runtime, database connectivity, configuration, a build artifact and an operational way to inspect failures. Learn the target platform’s process and check logs after release. Do not claim production readiness without verifying the environment, access controls and data-handling requirements.

Keep deployment guidance proportional to the course environment. A local or sandbox deployment can still teach configuration and release concepts, but should be described accurately and not represented as a hardened production system.

Present a credible portfolio capstone

A useful project README explains the problem, key features, technologies, setup steps, test commands and limitations. Include a simple architecture diagram or walkthrough where helpful. Be ready to explain why data is modeled a certain way, how errors are handled and what you would improve next.

AI can support brainstorming or code review, but verify every suggestion and disclose assistance according to the project’s rules. Never include confidential data. A portfolio is strongest when you can demonstrate understanding, not merely show generated output.