In the world of software development, ensuring that your application works as intended is crucial. This is where testing comes into play, serving as a safeguard against unexpected behavior and bugs. But how do you test effectively? This is where concepts like mocking, stubbing, and contract testing become vital. Let’s dive into these techniques and see how they can enhance your testing strategy.

• The Essence of Mocking and Stubbing

Mocking and stubbing are techniques used primarily in unit and component tests, but their usefulness extends beyond these. They are about creating fake versions of external or internal services to streamline and stabilize testing processes.

Mocking refers to creating a faux version of an external or internal service to replace the real one during testing. This is especially useful when your code interacts with object properties rather than behaviors. By mocking dependencies, you enable your tests to run more quickly and reliably since they are not bogged down by real-time data fetching or complex logic processing.

On the other hand, stubbing involves creating a stand-in for certain behaviors of an object rather than the entire object. This technique is useful when your implementation interacts only with specific behaviors of an object, enabling faster and more focused tests.

• Practical Applications

When your code uses external dependencies, such as system calls or database access, mocking or stubbing comes in handy. For example, instead of actually creating or deleting a file during a test, you can mock or stub the file system’s responses. This not only speeds up the testing process but also ensures that your tests remain independent and easy to manage.

• Contract Testing in Microservices

In a micro-services architecture, services interact based on predefined “contracts” detailing expected requests and responses. Contract testing verifies that these interactions meet the agreed-upon standards. Unlike traditional integration testing, contract testing focuses on the interfaces between services, making it a leaner and more targeted approach.

This type of testing is beneficial for checking the integrity and reliability of service interactions without deploying an entire system. It’s particularly effective in continuous integration pipelines, as it ensures that changes in one service don’t break the contract with another.

• The Role of Mocks and Stubs

In contract testing, mocks and stubs play a crucial role. By simulating the services that an application interacts with, developers can check the consistency and reliability of the application’s responses without relying on live services. This approach significantly reduces testing time and increases reliability.

• Conclusion

Testing is an integral part of the software development lifecycle, and techniques like mocking, stubbing, and contract testing are essential tools in a developer’s arsenal. By understanding and implementing these strategies, we can ensure that your tests are both efficient and effective, leading to more reliable and maintainable software.

The link to the initial blog post: https://circleci.com/blog/how-to-test-software-part-i-mocking-stubbing-and-contract-testing/

From the blog CS@Worcester – Coding by asejdi and used with permission of the author. All other rights reserved by the author.

https://www.javatpoint.com/difference-between-object-oriented-testing-and-conventional-testing#:~:text=In%20object%2Doriented%20testing%2C%20the,encounter%20in%20the%20real%20world.

Object Oriented testing focuses on the behavior of an object or class within an  object oriented system with its goal being to ensure any interaction or expected behaviors are correctly carried out when the system is used. This type of testing also heavily relies on ‘real world’ situations for example when writing test cases you would base your cases on things that the system is likely to interact with in the real world input wise which makes it easier to identify any possible behavioral issues within the system. There are three different testing phases which are commonly used in object oriented programming which are Unit testing, integration testing and system testing. Unit testing is used when testing classes or objects while isolated from the rest of the software, integration testing is used to verify that objects or classes are able to properly work together and system testing is used when testing an entire system to verify that the system itself works properly. Object oriented testing also requires the tester to have knowledge of a system’s inner workings in order to properly test it. This type of testing obviously is used on object oriented software which would be any software that is written using an object oriented language so in that aspect it is limited as object oriented testing would not be relevant or necessary to software which is not in the object oriented domain.

I found this article useful as it gets straight to the point when it comes to outlining the basic idea behind object oriented testing but it also chooses to compare it to conventional testing which helps the reader to differentiate between some of the different attributes of the two testing types as well as some of their similarities. This really helps when it comes to understanding why object oriented testing is defined as its own type of testing when their are already many other testing methods. This article helped me gain a greater understanding of object oriented testing which will help me to think more in depth about the cases I am able to write while testing in the future. The specific use of real world scenarios mentioned in this article was particularly interesting to me as I had not previously thought about how using real scenarios would typically provide more accurate results and or errors as compared to randomly selected scenarios which will also influence how I write test cases in the future.

From the blog CS@Worcester – Dylan Brown Computer Science by dylanbrowncs and used with permission of the author. All other rights reserved by the author.

This week I decided to touch on static and dynamic testing. I wanted to find a blog that talks about different techniques for each type of testing. While searching I found the blog Static Testing VS Dynamic Testing – Key Differences testing by Kiruthika Devaraj. It first begins by generally defining the two types of testing. Devaraj first defines static testing as involving “testing the software without running it” and dynamic testing as involving “executing the program and testing its behavior in different scenarios”. They then go on to explicitly outline the differences in the next section. 

In the section titled “Differences Between Static Testing and Dynamic Testing”, they explain that static testing involves reviewing the documentation, design or code to check for defects and errors without executing the software. They also mention that this type of testing aims to identify issues early in software development when they are easier and less expensive to fix. They explained that dynamic testing involves executing the software and testing its behavior in different scenarios. They explain that during this process testers create test cases and run tests to identify defects and ensure the software meets the specified requirements. By testing the program against a variety of inputs, expected outputs, and error scenarios, this kind of testing essentially tries to evaluate the product’s functionality, performance, and security and ensure that it operates as predicted. In this section, the author uses bullet points and spacing to organize the information and make it easier to digest. 

The next section is a table that clearly outlines the differences in features like definitions, objectives, types of testing, timing, and results for static and dynamic testing. Static testing involves code review, walk-through, and inspection while the other involves unit, integration, system, acceptance, performance, security, and user acceptance testing. The author then explains how to choose between the two types of testing. They mention that it comes down to the specific requirements, objectives of the testing process, and desired outcomes. I enjoyed this article because the author got straight to the point when writing the article while clearly defining the use of each type of testing. I think the way they went about writing this informational piece was effective in keeping the reader’s attention by outlining the information in the simplest way possible. The use of bullet points and tables helped me to remember the information better. I am likely going to have to test my future personal projects so knowing the differences between the two types of testing and how to choose it was important to read about.

From the blog CS@Worcester – Live Laugh Code by Shamarah Ramirez and used with permission of the author. All other rights reserved by the author.

Unfortunately, just weeks after exploring test-project’s blog, it was shut down and replaced by a new blog in Tricentis. I was disappointed to find that I could no longer utilize this great resource. In searching however, I found that test-project was on its way out as far as automated testing goes, and this reaffirmed to me the ever changing world of software. In response I looked for blogs to improve my testing skills individually once more, and explored stickyminds again. The article I chose explained how practice can affect your software testing skill. The article’s title is Repetition in Practice by Justin Rohrman.

The article explores the pivotal role of repetition and practice in software testing, just as you would need in any other interest, like music. The author mentions his own experience with music in fact, and used his practice with music to inspire his software testing strategies. The article highlights the significance of deliberate practice in honing testing skills, advocating for an adaptive approach that considers the unique context of each testing scenario. A large failing of many software developers is the lack of practice, or lack of practice strategies. The article delves into some various methods for practicing software testing, to show how it connects with other interests. The most effective method introduced in the article is the ability to relate your own testing specific goals to other interests. For example: Heuristics is mentioned, and the author mentions if you like ‘Scrabble’ to play scrabble while thinking of heuristics and apply them to Scrabble. This will improve your skill in heuristics and allow you to improve it in your testing. This is an excellent strategy to help the mind focus on coding when other interests take priority. Overall the article emphasizes the importance of maintaining a growth mindset and embracing opportunities for learning and experimentation to refine testing proficiency. Outside of the article itself, there is a very valuable comment made by Michael Larsen. This comment mentions how testing is more subjective and abstract, and that it’s important to persevere when practicing. This is because unlike things like music, there are very little measurable improvements that you can see yourself, and you often have to wait for others to notice the improvements. This was an excellent thought provoking comment.

Through the CS-443 course, I have been considering how testing will play a role in my professional career and how I will develop these skills along with development. These kinds of practice strategies give me an engaging way to turn my brain into the mindset to code. This is very valuable to me, as often I can get very distracted. To be able to turn distractions into sources of thought for my own coding will be absolutely essential to developing my skills further. I chose this blog because I wanted to hone my skills but lack strategies to do so, and this applies beyond just testing for me.

Source:
https://www.stickyminds.com/article/repetition-practice?page=0%2C0

From the blog CS@Worcester – WSU CS Blog: Ben Gelineau by Ben Gelineau and used with permission of the author. All other rights reserved by the author.

This week’s blog topic was chosen simply because I had zero knowledge of the term/topic. Jake Holy’s blog on Stochastic and Property-Based Testing blog covers the uses of each testing and effective tools used to implement each. Stochastic testing refers to the testing method that uses random inputs to test the behavior of the program. Rather than using predetermined inputs for test cases, stochastic utilizes various inputs to find unexpected bugs in the program. The main purpose of stochastic testing is to uncover errors through unorthodox methods. This method offers a wide testing coverage by testing all possible scenarios that might go unnoticed with traditional testing.

Property based testing refers to the method of testing by describing what the software should do rather than creating distinct test cases. Initially, testers establish properties that describes what the code should always do. This method of testing utilizes tools that automatically generate test cases to test these properties. After generating these cases, random inputs are utilized to ensure the properties are followed. The benefit of this testing method is testers are only required to declare the properties the code must follow. Instead of manually creating test cases and risking the possibility of missing a test case, these cases are generated for them. Property based testing guarantees effective test coverage. Additionally, this form of testing allows testers to focus on the abstraction and behavior of their code, allowing for a more robust product.

The blog goes in-depth with various testing tools, for example, a testing library created for the Haskell programming language called QuickCheck, which allows developers to write properties for their code which is automatically generated into test cases. QuickCheck has become a very popular tool to use for testing due to its automation, which is why it has been ported to other programming languages like Python and Scala.

The second tool that the blog mentions is Stimulant for Clojure. Similar to QuickCheck, Stimulant allows developers to define properties and it automatically generates test cases to test your program.

One thing that is mentioned in both testing libraries is shrinking. Shrinking refers to the method of finding the smallest input of a failing case, allowing developers to precisely pinpoint areas in the program that is causing the test case to fail. It is crucial that developers know their program has failed a test case, but it is even more crucial that they know why, so they can effectively fix the problem.

Blog: https://blog.jakubholy.net/2013/06/28/brief-intro-into-randomstochasticprobabilistic-testing/

From the blog CS@Worcester – Computer Science Through a Junior by Winston Luu and used with permission of the author. All other rights reserved by the author.

PATH TESTING.

Path testing is an approach to testing where you can ensure that every path through a program has been executed at least once. However, testing all paths does not mean that you will find all bugs in a program. There are some steps involved in path coverage testing. Step one is code interpretation. It is important to carefully understand the code you want to test. The next step is constructing a control flow graph. It shows the nodes representing code blocks and edges for the movement of control between them. The third step is determining the paths. This entails following the control’s path from its point of entry to its point of exit while considering all potential branch outcomes. While determining paths, you’ll also consider loops, nested conditions, and recursive calls. It is important to list every route like giving each path a special name or label so you can keep track of which paths have been tested. The next step is testing case design. Create test plans for each path that has been determined, make inputs that will make the program take each path in turn. Make sure the test cases are thorough and cover all potential paths. Examine the test results to confirm all possible paths have been taken. It is important to make sure the code responds as anticipated.

Some advantages of path testing is it helps reduce redundant tests, it focuses on the logic of the programs and it is used in test case design. Some cons of using path testing is the test case increases when the code complexity is increased, it will be difficult to create a test path if the application has a high complexity of code and some test paths may skip some of the conditions in the code. There are three path testing techniques which are Control Flow Graph (CFG) – The Program is converted into Flow graphs by representing the code into nodes, regions, and edges. Decision to Decision path (D-D) – The CFG can be broken into various Decision to Decision paths and then collapsed into individual nodes. Independent (basis) paths- Independent path is a path through a DD-path graph which cannot be reproduced from other paths by other methods. I chose these two resources because they go more in depth about path testing and help explain it well. One of the sources talks about the pros and cons of using path testing, the types of path testing which I didn’t know before this.

References.

https://www.geeksforgeeks.org/path-testing-in-software-engineering

https://www.tutorialspoint.com/software_testing_dictionary/path_testing.htm

From the blog CS@Worcester – Site Title by lynnnsubuga and used with permission of the author. All other rights reserved by the author.

In the realm of software development, testing is a critical phase that ensures the quality and reliability of the product. This week, my exploration led me to a compelling resource that sheds light on an integral part of testing methodologies: the use of stubs. Stubs are simplified, replaceable components that mimic the behavior of real software modules, allowing testers to isolate and test individual parts of a program. The resource, an insightful article titled “A Comprehensive Guide to Stub and Mock Testing: Unveiling the Essence of Effective Software Testing” provided a comprehensive overview and practical advice that I found particularly enlightening.

The reason I selected this resource was its direct relevance to our current course material on software testing methodologies. As we delve into the complexities of ensuring software reliability, understanding the role of stubs becomes indispensable. This article not only introduces the concept but also illustrates its application with clarity and precision, making it an invaluable tool for beginners and seasoned developers alike.

Upon reading, I was struck by the depth of information presented. The article begins by defining stubs and differentiating them from other testing techniques such as mocks and drivers. It then delves into practical scenarios where stubs can significantly enhance the testing process, such as in unit testing and integration testing. The step-by-step guide on implementing stubs, complete with examples in popular programming languages, was particularly useful.

Reflecting on the content, I realized the importance of stubs in creating a controlled test environment. By simulating specific components, stubs enable testers to pinpoint errors more efficiently and focus on testing the functionality of individual units without the complexity of the entire system. This not only streamlines the testing process but also improves the accuracy of test results.

The application of what I learned from this article to my future practice is clear. I anticipate using stubs to conduct more effective and efficient testing, particularly in complex software systems where isolating components can be challenging. The hands-on examples provided will serve as a reference guide as I implement stubs in my projects.

For those interested in diving deeper into the subject, I highly recommend reading “A Comprehensive Guide to Stub and Mock Testing: Unveiling the Essence of Effective Software Testing” This resource has significantly enhanced my understanding of stubs in software testing and equipped me with practical skills that I look forward to applying in my future endeavors.

Resource Link: https://medium.com/@fideraphael/a-comprehensive-guide-to-stub-and-mock-testing-unveiling-the-essence-of-effective-software-testing-7f7817e3eab4

As we continue to explore the vast landscape of software testing, I am excited to share more discoveries and insights. Stay tuned for more reflections and learning experiences.

From the blog CS@Worcester – Abe's Programming Blog by Abraham Passmore and used with permission of the author. All other rights reserved by the author.

Understanding Mock Objects: A Journey from Confusion to Clarity

When I first stumbled upon the concept of “mock objects,” it was during my foray into the Extreme Programming (XP) community. The term has since become more prevalent, particularly among those versed in XP-influenced testing literature. Yet, mock objects are frequently misconstrued, often mixed up with stubs, which serve as basic aids in testing environments. This confusion is understandable;

Mock objects represent a nuanced divergence in the realm of software testing, embodying both a shift in test result verification—state versus behavior verification—and an ideological split in testing and design methodology: classical versus mockist Test Driven Development (TDD).

Diving into Testing Styles

To elucidate, let’s consider a straightforward example: testing an order system interacting with a warehouse. In traditional state verification tests, we’re primarily concerned with the end-state of the system under test (SUT) and its collaborators after the exercise phase. Here, both the SUT (Order) and a real collaborator (Warehouse) are employed, focusing on the system’s final state to verify test success.

Conversely, tests utilizing mock objects—like those in the jMock library—adopt behavior verification, emphasizing the interactions between the SUT and its collaborators. Instead of a real warehouse, a mock warehouse is used, setting expectations for how the SUT should behave. This approach focuses not on the final state but on ensuring the SUT makes the correct calls to its collaborators.

Exploring Classical vs. Mockist TDD

The distinction doesn’t stop at test execution. It extends into the philosophy behind the testing approach. Classical TDD practitioners utilize real objects where feasible, employing stubs or mocks primarily for cumbersome collaborators.

Mock objects are born from the XP community’s focus on TDD, where design evolves through test iterations. This “need-driven development,” particularly championed by mockists, advocates for outside-in programming, starting from the topmost user interface layer and working inwards, designing the system piece by piece.

Fixture Setup and Test Isolation

Fixture setup and test isolation further differentiate the two approaches. Classic TDD often involves extensive fixture setup, creating the SUT along with all necessary real collaborators. Mockist TDD, by contrast, requires only the SUT and its direct mock collaborators, potentially simplifying test setup

Design Implications and Personal Reflections

The decision between classic and mockist TDD extends beyond mere testing strategy; it influences design philosophy and system architecture. Mockist TDD tends to encourage more decoupled, modular designs, as each component’s interactions are explicitly defined and isolated.

As someone who initially grappled with understanding mock objects, I’ve come to appreciate their value in elucidating system behaviors and fostering thoughtful design. Yet, the choice between classical and mockist TDD ultimately depends on individual project needs, team preferences, and the specific challenges at hand.By understanding the nuances between these approaches, developers can make informed decisions that best suit their projects, fostering environments where quality software can thrive.

Based link: https://martinfowler.com/articles/mocksArentStubs.html

From the blog CS@Worcester – Coding by asejdi and used with permission of the author. All other rights reserved by the author.