In my expirience there is the importance of having granular tests, especially in an object-oriented paradigm. It also highlighted that sometimes, logical errors can manifest in non-obvious ways. The combination of systematic testing and strategic debugging made all the difference.

Unless it's a really simple and straightforward bug, the first step would be to create a test case for the bug, which would fail initially. The test case specifies clearly what the expected behavior should be. The goal of debugging is then set - to pass the test case. Th is is the Test Driven Development approach. It's tempting to dive right in to debug the code, but without a clear definition of that "fixed" means, one could be wasting time fixing irrelevant coffee or creating incorrect fixes. Defining a test case also means building up a repository of automated tests so that regression testing can be accumulated, ensuring that the same bug doesn't reappear embarrassingly later on.

Unit tests are your first line of defense. Mock your injected class dependencies and run through each public method with a variation of those dependency behaviors. The goal is ultimately to mock the output of the public methods given the dependency behaviors. This includes when they throw exceptions. Note to always follow proper DRY and coding practices in unit tests to keep it at a high bar and so teammates are highly motivated to write them. We tend to not give them too much love.

Errors in Object Oriented testing, here I would assume we are focused on run time errors. Compile time are mostly easy to fix, your IDE does most of the work for you. So for runtime errors, its not easy to track down unless we are able to reproduce them. One we have repro, can be take care off. For intermittent errors, you may have to do a bit more. I would think of code review and logging to avoid and for a fix.

Read the error message carefully. Isolate the Problem: Check OOP Concepts: Encapsulation: Ensure that data is protected and can only be accessed or modified through the appropriate methods. Inheritance; Make sure child classes correctly inherit properties and behaviors from parents. Polymorphism: Confirm that objects are using the correct method. Abstraction: Check that abstract classes or interfaces are properly defined. Look for Common Pitfalls: Initialization: Ensure that all objects are correctly initialized before use. Visibility: Check if methods and attributes have the correct access modifiers (`private`, `protected`, `public`). Use Unit Test: Write unit tests.

These principles are the base of good design and less errorprone programs. But in practice it sometimes leaves room for over engineering and premature optimization. All programs are builds for existing requirement and assumptions of possible future changes, but if new requirements fall out of assumptions than applied principles might create a problem. So the best way is to apply OOP principles when obvious and use KISS when uncertain,this will reduce analysis paralysis and give some flexibility.

On a one project, we were integrating with several third-party payment gateways. Instead of cluttering our payment processing code with conditions for each gateway, we utilized encapsulation and abstraction. We designed an interface, PaymentGateway, with methods like processPayment and refund. For each third-party service (e.g., PayPal, Stripe), we then created a class that implemented this interface. When a user chose a payment method, the system would instantiate the corresponding class and all further operations would just call the interface methods. This approach isolated the quirks of each service, made our main codebase cleaner, and simplified adding new gateways in the future.

OO Testing itself implies that best practices should adhere object oriented principles. System should follow SOLID principles, testing will be carried out in such a way TestCase is not depended, TDD is well defined. More emphasis should be also given in code refactoring that one method should not clutter all the code.

Don't go crazy mocking out every layer of your application. It often doesn't make sense to wrap a test around every method in every object. In our multi-tier server application we provide a fully functional database to our middle tier objects, which is generally a recent copy of de-identified production data. That is a valid 'mock' in my opinion. Our middle tier consists of chunks of business logic that interact with a database and manipulate objects in memory. The granularity of our unit testing centers around those chunks of business logic, which a functional database on one end, and then the front end is mocked out by simply directly calling methods on those business objects. Shoot for greatest bang for the buck from test code.

MoQ and FAKE are best to implement and easy to well. It will make testing and writing test cases easier. TDD based SpecFlow is another way to use so that Business Analyst\Tester work in conjunction with Developers

Good testing also increases confidence in major refactors of code. Increases confidence in deploying complicated applications where testing all components is difficult. Good testing can also allow quick exploration which I've found invaluable in new feature development and performance tuning

In a project aimed at web application development, we faced challenges in ensuring consistent behavior across various browsers. We adopted Selenium, a testing tool tailored for web applications. Leveraging its WebDriver, we scripted scenarios mimicking user interactions across Chrome, Firefox, and Safari. The real breakthrough came when we integrated Selenium with JUnit. This enabled automated browser tests to run as part of our regular unit test suite. Consequently, every code change triggered a thorough assessment, catching regressions early. The efficiency of this tooling reduced manual QA effort and led to a more reliable application release process.

In my experience, users not being able to see accounts linked to their updated profiles was a major issue, causing a UX bottleneck. The bug was traced back to a LiveData object, which was supposed to auto-refresh the accounts list on profile updates. Instead of deep-diving into endless debugging, JUnit and Mockito were brought into play. Quick and effective test cases were written, and the LiveData issue was promptly spotted. The bug was efficiently squashed. This not only improved UX but also highlighted why structured testing often beats random debugging.

Refactoring also helps you understand the code - going through and changing small things in the code helps me understand the code far more than just reading it. So often refactoring is one of the first steps I take since it provides a double benefit: I learn the code while doing it, and future developers can understand it easier

During a project, I noticed that our OrderProcessing class was becoming a monolith, handling everything from validation to database operations. This made unit testing challenging and debugging painful. Applying the Single Responsibility Principle, I refactored the class: extracting the validation logic into a OrderValidator class and database interactions into an OrderRepository class. This not only made the code more readable but also modularized testing. For instance, I could mock the repository while testing the validator. Using IntelliJ IDEA's refactoring tools, the process was streamlined, ensuring references were updated across the project without manual intervention.

Refactoring the code to simplify it and introduce principles such as SOLID can really help to highlight code smells and is a golden opportunity to introduce low level unit tests and improve error handling.

In my experience, refactoring proved invaluable for simplifying complex code. The module could initiate payment flows in multiple ways, initially requiring consumer apps to manage configurations on their end. This approach led to added complexity and more 'glue code.' Through refactoring, we encapsulated these different starting flows into a single data class, allowing consumer apps to easily choose their preferred flow. This reduced code complexity and improved both testability and maintainability, reaffirming the power of structured refactoring.

Refactoring is a great tool that improves your code. But should go hand to hand with prior testing plan that verifies code before refactoring and after. Whether it's e2e, unit tests or manual testing (better all together).

At a past project, we developed a complex algorithm for product recommendations. After initial implementation, I submitted my code for peer review on GitHub. My colleague spotted edge cases I had missed by reviewing the pull request. Additionally, he suggested clearer variable names for better readability. During this process, I also realized that while the algorithm was clear in my head, others might struggle to grasp its nuances. So, I incorporated inline comments explaining tricky segments and used Javadoc for method-level documentation. After merging, we used Doxygen to generate a comprehensive document. This not only made onboarding faster for newcomers but also eased future modifications.

Too long to describe here due to character limit but I don't believe in mocking frameworks. A unit-test should not be seen as testing a method in comporte idolation. Methods/functions/behaviors should be tested by observing how it's behaving, not by dictating how it's interesting with other methods. I believe unit-tests should run as close as possible to a production-like state. Scenario-based tests should be written as unit-tests. This way as provided me the ability to write pretty much bug-free applications.

Commit yourself when you write tests - test the right things, it does not help to create test cases to reach 70%, 80% or even 90% code coverage .. when the only things you test are all your getters and setters. Check what the code coverage you have really covers and when there are complicated algorithms write testes especially for these, and when you read your code, always overthink your comments, especially if you workaround things or have uncommon code parts.