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via Udemy |
Go to Course: https://www.udemy.com/course/hands-on-software-testing-in-python-w-unittest-framework/
Certainly! Here's a comprehensive review and recommendation for the Coursera course "Hands-On Software Testing in Python": --- **Course Review and Recommendation: "Hands-On Software Testing in Python"** Are you a beginner looking to dive into the world of Python testing? Or perhaps a software professional seeking a thorough, practical guide that covers both concepts and real-world scenarios? Look no further! *"Hands-On Software Testing in Python"* on Coursera is exactly the course you need. **What Makes This Course Stand Out?** 1. **Beginner-Friendly Yet Comprehensive**: Designed as a 'getting-started' guide, this course effortlessly transitions from fundamental concepts to complex testing scenarios, making it suitable for newcomers and experienced developers alike. 2. **Rich Real-World Case Scenarios**: Ahmed Alhallag, the instructor, emphasizes learning through building. The course features multiple cases where you'll analyze existing codebases and build mini-projects from scratch, following a systematic approach. This practical focus ensures you understand not just the theory, but how to apply it in real projects. 3. **In-Depth Coverage**: - Starts with the basics of software testing and best practices. - Covers both procedural and object-oriented programming paradigms. - Introduces concepts of code modeling, UML design, and implementation techniques to develop clean, maintainable systems. - Demonstrates end-to-end testing strategies, including unit, integration, and mocking techniques. - Explains test-driven development (TDD) and how to use Python's built-in `unittest` framework efficiently. 4. **Systematic Approach to Testing**: The course enhances your understanding of what constitutes a 'unit' of testing, how to mock dependencies, and how to generate coverage reports—crucial skills for ensuring code quality and robustness. 5. **Interactive and Guided Learning**: Through code-alongs and mini-projects, you'll get hands-on experience. The instructor explains why thorough understanding of your system is essential before jumping into writing tests, fostering best practices. 6. **Focus on Code Quality and Maintainability**: Beyond just writing tests, the course explores quality of modeling, coding standards, and the importance of modular, well-structured code—key factors that help avoid the pitfalls of refactoring and project reboots. 7. **CLI and Debugging Skills**: Learn to use the command line and Python's debugger to inspect, debug, and test your code in real-time—valuable skills for any software engineer. --- **Who Should Take This Course?** - Freelancers, college students, and new software engineers venturing into testing. - Developers who want a systematic, comprehensive understanding of how to test Python applications thoroughly. - Anyone interested in learning how to build reliable, maintainable, and testable Python code from scratch. **Final Verdict** *“Hands-On Software Testing in Python”* is an excellent choice for anyone eager to master Python testing from foundational principles to advanced practices, grounded in real-world scenarios. Its practical focus, combined with detailed walkthroughs and project-based learning, makes it an invaluable resource to elevate your software testing capabilities. **Highly Recommended**: If you are committed to writing robust Python code and want a course that covers all aspects—from theoretical foundations to practical implementation—this course is a definite go-to. --- Feel free to enroll and start your journey toward becoming proficient in Python testing, ultimately making your software more reliable and maintainable! --- Would you like a checklist of what you'll need to prepare for this course or steps to get started?
Looking for a beginner-friendly, 'getting-started' guide, that happens to ALSO be as comprehensive as possible with rich real-world case scenarios that COMPLETELY covers the all aspects, the ins-and-outs, the nits and grits of Unit and Integration testing Python? Yes dear, you are at the right place.Welcome to "Hands-On Software Testing in Python"! If you are a freelancer, a college student, or a software engineer, at some point it's just inevitable to not run atleast some 'exploratory testing' scripts to inspect and test various edge cases, just to make sure how solid your work is, but things starts to go downhill when a certain component breaks down, it's either you go into a refactoring purgatory, or you start the project again from scratch! In this course I will show you multiple case scenarios, where we will have existing code bases, as well as mini-projects that we will build from scratch in a code-along fashion, what matters the most is that we will follow a systematic procedure to analyze and design our test cases, for each case scenario, and then implement them.My name is Ahmed Alhallag, I'm a Software Engineer and an Assistant Lecturer. I will take you through an intensive journey starting with the theoretical concepts behind Software Testing, up to the inner-most parts where you will learn the best practices in approaching any project, designed and implemented in any paradigm (Procedural & OOP are covered in this course), no matter how large or big it might seem from the surface.Writing code isn't supposed to be the main point of focus for you as a software developer, especially code that just 'works' for a current point of time. A bunch of other factors needs to be taken into consideration such as:Quality of modeling: You might think: "alright, I have this task to implement, so let's just wing it!", and you immediately hop on to your favorite IDE and starting coding. For certain tasks, maybe this would be the time-efficient thing to do, for larger one, this will eventually consume your time on the long run, because you will keep going back in forth in:encapsulating this block of code, and refactoring that block of code, saving your sensitive data in a.env file after it was thrown away at the top of your script when you started working, serializing this output, and persisting that output into a json file instead of a basic txt file, creating a middleware, an API interface or a certain controller that you suddenly found out that you need to perform some action, implement a data handler because the code became so redundant and intertwined, changing up the entire set of data structures used, which as a result, changes the entire processing logic written ahead of time as well!See, all of the previous scenarios, are just daily events that we go through in our development journey, if you haven't faced any of them yet, that's good news! We will briefly cover a systematic way to implement our code via analysis (OOA) and design (OOD using UML), with the help of mini-projects that we will build together!This is obviously not the focus of this course, so we won't be spending much time on that part, we will take we need to get to have a clean code base later on when we implement.Quality of code: After the brief concepts on conceptual designs and modeling, we will follow the guides (class diagrams) we created to implement the core system we will test bit by bit later on. We will be using Object Oriented Programming (OOP) for the majority of our implementations.You might ask yourself, why would we spend THIS much time at the implementation of every use case? why not just take the code as it is and start writing test cases?That's a valid point, but hear me out: I believe that if your want to know the ins and outs of a system, whether you are performing a defensive/offensive security measure, designing a UI/UX, implementing a database layer, or executing some test cases, you need to be FULLY and THOROUGHLY aware of the system you are working on. For experienced developers, they can definitely pick the pace off at any point in time and start implementing their test cases, but for anyone who has not written a lot of test cases before, or at the start of their journey, this would be a hassle! not knowing what exactly that needs to be tested, or even not knowing how or where to start!This is the basis of the approach I'm following in most of the given case scenarios, where basically we will build the end-system we would want to thoroughly test from scratch! After having a brief on best practices in code/system modeling, and implementing these systems manually, we will dive deep into testing, where: We will mainly use the unittest framework in Python, which is a built-in easy to use module to get started as quickly as possible.We will start implementing the mini-project in a Procedural manner (using the most basic modularization approach; functions).We will then how to design and implement Unit Tests for our procedural code, by setting up our terms of agreement on what exactly should be defined as a "Unit", as well as configuring of new project hierarchy (tree of folders). We will have a walkthrough into one of the most common approaches used for testing; Test Driven Development, or TDD for short.We will then have a Paradigm shift, where we travel to the OOP realm. I'm assuming you have a fair background in OOP, a brief recap will be given. (watch the promotional/intro video)We will write Unit Tests for Object Oriented (classes) that we have created, and an update to our "unit" definition will take place.We will see how useful your knowledge of the Command Line Interface (CLI) can be, by using your terminal session to inspect and debug your unit tests in real-time using the Python Interactive Debugger.We will have real data, and we will learn how to sample them and/or fake them when neededWe will design and implement interrelated modules, we will also design and implement the relationships between them. And will know that by doing so, another different added layer/type of testing needs to be performed, which is Integration Testing, to make sure that integrated components, work as intended.We will see a lot of coupled dependencies, which will lead us to isolate them completely using "Mocking" to perform our unit tests.We will learn how to deal with intermediary or pass-through code, as it sometimes becomes unclear to whether you should test them or not.We will learn how to generate test coverage reports (in HTML) and how to run multiple (unit and integration) tests simultaneously.Finally, we will have an intensive walkthrough on files handlers and operations (such read, write, etc..) as well as the common stdout and stdout functions (such as print, input) and how to exactly test them for full coverage.