Mastering JUnit 5 and Mockito: From Basics to Advanced Testing
This course is designed to help software engineers master the art of unit testing and mocking with JUnit 5 and Mockito
Development ,Software Testing,
Lectures -33
Duration -2.5 hours
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Course Description
This course, which covers in-depth mastering by developers, testers, and software engineers the skills of unit testing and mocking using JUnit 5 and Mockito, provides examples, explanations, and best practices for learning in detail unit testing, test-driven development, TDD, annotations, assertions, mocking techniques, and exception handling. If you are a fresher to this field or even an expert wanting to refine your skill, then it is what you need to achieve robust and reliable applications.
Goals
- Understanding the basics of JUnit 5 and its differences from previous editions.
- Unit test writing with JUnit 5 annotations and assertions.
- Understanding Mockito with roles of mocking dependencies
- Application of Test-Driven Development to make code more reliable.
- Testing a Spring Boot application with JUnit 5 and Mockito
- Exception handling, method mocking, interaction verification with Mockito
- Database-related testing with JPA and ensuring data integrity.
- Integration tests in ensuring the correctness of end-to-end functionality.
- Troubleshooting test failure and enhancing quality of the applications.
- Best practice for test automation and writing good quality test cases.
Prerequisites
Basic understanding of Java programming and object-oriented concepts.
Familiarity with IDEs like IntelliJ IDEA, Eclipse, or Java IDE.
A basic understanding of database operations (JPA or JDBC) is helpful but not mandatory.
Eagerness to learn practical unit testing and mocking techniques.

Curriculum
Check out the detailed breakdown of what’s inside the course
Foundations of Unit Testing and JUnit
3 Lectures
-
Introduction to Unit Testing and Test-Driven Development (TDD) 08:41 08:41
-
Getting Started with JUnit: What, Why, and How 03:12 03:12
-
Essential Annotations in Java for Effective Testing 03:23 03:23
Advanced JUnit Annotations and Testing Flow
3 Lectures

Assertions and Best Practices in JUnit
4 Lectures

Building Your First Tests with JUnit and Mockito
5 Lectures

Mocking and Dependency Management with Mockito
3 Lectures

Testing the Service Layer with Precision
6 Lectures

Exception Handling and Edge Case Testing
3 Lectures

End-to-End Testing Strategies and Best Practices
3 Lectures

Bonus and Advanced Insights
3 Lectures

Instructor Details

AKHIL VYDYULA
Data Scientist | Data & Analytics Specialist | EntrepreneurHello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.
My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.
I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.
Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.
Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.
I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!
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