Dissecting Automated Functional Testing — How to Learn Automated Testing in 2022?
Functional testing feels like the bane of one’s existence when things are running on a deadline. No matter the codebase or structure, there’s just so much to do. It also takes a ton of time if you’re doing it manually, and the inefficiency is an atrocity.
As one of the final steps before deployment (usually before the non-functional tests start), we have to test everything relating to the functional requirements of the software. That includes a plethora of different methods and approaches.
Fortunately, much of the world has moved on to automated functional testing to make the process effortless (still not enjoyable, though).
That’s what we’ll be focusing on in this piece today. Along with the logistics of functional testing, we’ll attempt to learn the approaches to automated testing and look at the tools at our disposal. Let’s get started.
Exploring Functional Testing — What It Is (and Isn’t)
It’s essential to understand functional testing and dissect its differences from non-functional testing. That’s because it helps us plan the whole process with ease. After all, functional testing requires us to plan ahead and utilize the resources efficiently.
Think of it like this — the software you develop has many set requirements for functionality. For example, a specific interaction with the UI would result in a certain change. Functional testing primarily ensures that each of these parameters works flawlessly.
Programmers even refer to “Functional Testing” as a black box test (although that’s not all it is). That means you are primarily concerned with the software’s input and output. What goes on in between is not a huge concern.
Think of it from a user’s perspective for explanation’s sake. A user doesn’t know or care about how you’ve reached a certain solution. They’re more concerned with getting the output they expect from the input they give the software.
You’d test how each module functions, whether it properly interacts with the rest of the modules, how everything works, etc.
There is a misconception, however. Functional testing is not the same thing as a user using the software. A user uses it in an unpredictable environment in conjunction with other applications. There are too many variables in that context to be the same as functional testing.
Functional vs. Non-Functional Testing
So, why is this distinction crucial? For one, the whole planning ahead part relies on this understanding. Designing the test also requires sufficient knowledge of both these aspects.
While functional testing is concerned with the output or functionality of the software, non-functional testing cares about how that functionality is achieved. Of course, a product requires a combination of both to reach the intended results.
Let’s say clicking on a portion of the UI does a task. Functional testing will check if the job is done correctly without worrying about what’s going on under the hood. Non-functional testing, on the other hand, would look at the performance and workflow instead.
That’s not the end of it. Things like how scalable the software is and its performance under load fall under non-functional testing.
Of course, our primary concern is functional testing today. So let’s look at a few methods and types of functional testing.
Different Approaches to Functional Testing
Functional testing isn’t limited to a single streamlined process. There are different types of it, and it’s easy to see why. Each software has a unique design and functional side that won’t match others. It also has different elements inside, most of which require testing.
Typically, people divide this process into four stages. These include unit tests, the integration between them, a test for the whole system, and acceptance testing as the last step. These are streamlined stages for convenience, and you’ll find different categorizations in different places.
The smallest functional piece of a program is what you call a unit. That means this is a test in the most basic stage of development, making this test somewhat different from other functional tests.
One, this test would be typically done by the developer. As a result, this would technically be a white box test. This test helps the early development stage shine. Plus, it reduces the chances of bugs in the later stage — saving you time and money both.
For instance, statement coverage is a common technique for unit testing. That means every function’s statement will be tested thoroughly by the dev. Of course, there are other options, such as method coverage or line coverage, but that’s a story for another day.
Most developers are familiar with the term regression (primarily due to the headaches it causes). This test ensures that the whole codebase is still functional after committing a new change or adding a new function. Doing this after each significant change is highly beneficial, as looking for bugs and resolving them becomes much more effortless.
This test method often works in conjunction with unit testing. While unit testing tries out individual pieces of the software, this test checks whether all of the units are working well together. For example, how they interact with each other and whether the microservices are working flawlessly can be confirmed by integration testing.
Also known as Build Acceptance Testing, smoke testing ensures that the basic and critical functionalities of the program are working well. As you can imagine, this is more of a preliminary test, and it does not concern itself with minute details and functionalities. Think of it as the green light (or red if you’re unlucky) for your build. If this test doesn’t go through well, there’s no point in advancing to other tests.
Sanity tests share some similarities with smoke tests because they both act as a primary check of the build. The distinction is that, unlike smoke tests, it mostly focuses on parts where the changes have been made. Furthermore, sanity tests are comparatively less planned ahead.
Most of the tests above look at smaller parts of a program and test them. System test, on the other hand, takes on the whole thing. This test occurs later in functionality tests, usually right before we proceed to acceptance testing.
Unlike integration or unit tests, we look at the program as a whole and determine its functionality that way.
Acceptance tests appear as the final tests in the line. These tests look at the whole thing from an end user’s perspective — trying the entire software out and testing the end experience. These tests are often categorized into phases such as alpha and beta. But the steps within those vary depending on the people developing the software.
Acceptance tests sometimes also include actual users in real-world use cases, as scripted tests aren’t realistic. However, the previous tests only test things within a sandbox, so this stage can take responsibility for getting out of that.
These are some of the most common types of functional testing. Unfortunately, there aren’t any textbook outlines for these, so the terms and definitions vary. The end goal and results remain similar, however.
Automated Functional Testing — When Functionality Meets Automation
As you’ve noticed (or worse — experienced) by now, functionality tests can be pretty tedious. And if you’re constantly churning out new builds and commits to the code, I feel for you.
The essence of automation testing lies here — automating any step you often repeat to make things faster and more efficient. It’s designed according to the program’s needs so that the workflows can differ from each other.
The question is whether you can justify re-designing your whole test script and automating it. After all, it will initially take a bit of an overhaul.
In short, yes. Here’s the broader version:
Benefits of Automation Testing
The first benefit of automation is that you save a ton of time. Let’s say the development team just committed a new feature to the code. Suffice it to say that manually testing the software after each change will be a chore and a half.
With automation, you can cut down on time needed to make those tests. It’s also easier to validate each build and quicken the feedback cycle. In turn, the whole development cycle becomes faster, you get to release quickly, and the overall efficacy increases.
Cost Efficiency: Along with time comes the aspect of cost. Basic math says that the more time testing takes, the more money we burn in logistics. Automated tests take much less time (especially if the tests are repetitive, which they typically are). Hence the process spends fewer resources on it.
Sure, the automation process itself will take resources. But as you keep utilizing it, again and again, that cost seems measly compared to what it would be with manual tests.
The modularity and reusability of these tests are amazing. Let’s say you design the test for a program. You can keep using it for most of that program’s lifetime without any issue. And secondly, you could also utilize parts of it for other programs, depending on how many similarities they share.
No Human Errors: Manual testing creates the possibility of human error, which would be evident with little experience. That’s entirely normal, but the issue is that it skews the results. Functional testing aims to ensure that there are no flaws from the test’s side of things. That way, it can ensure consistency and create a better output.
Test automation eliminates the chances of skewed results in your development. You’d be reusing the same code and preventing unaccounted flaws from the test.
From the previous point comes the accuracy of insights. For example, let’s say that a certain part of the UI failed to function as intended. On an automated test, the insights would contain every little detail and categorize them in a manner that makes problem-solving infinitely easier. You could easily look at all of the variables and catalysts that resulted in the error and resolve the issues.
Feature-Rich Test Designs
When doing things manually, there’s a limit to how much you can do without wasting a lot of time and resources. That reduces the efficiency of the test, as we wouldn’t get to try out all of the scenarios.
Automation broadens the horizon here. You could emulate all sorts of scenarios without having to worry about the logistics behind them.
As you can see, automation barely has any significant drawbacks when done right. However, the question is whether automating every bit of the process is rational or not.
Which Parts of the Test Should You Automate?
Here’s the thing — automation takes a decent chunk of resources, at least in the initial stage. Not every part of it would be cost and time-efficient. How do you determine which elements to automate, then? Here’s a short checklist that can help.
Any test that you perform again and again can and should be automated. The reasoning is simple — automating the test once would save you from manually repeating all of these tests repeatedly.
For example, smoke tests occur many times during the development cycle. Since we perform that test every time, we commit a new feature to the build, automating that would be quite beneficial.
There are some tests that require more work from the tester. Not only are these tedious, but the chances of human error are usually higher. Automating these tasks makes sense and can make things convenient in the long run.
Many of our tests run on multiple operating systems, platforms, or browsers, depending on the program you’re developing. That means the same functional tests need to run on each of them, taking up a lot of time. These are suitable cases for automation and are easier to scale.
You should now have an idea about which tests would be logical to automate. However, there are tests that you would run only a few times in the development cycle. It’s up to you to determine whether the effort of automation would be worthwhile for such scenarios. In many cases, it’s not, and the devs tend to do them manually.
This is an aspect that changes with each application, its scale, and the test frequency. Hence, you will have to be the judge of that.
Popular Tools for Automated Functional Testing
Much like how each software requires a different testing path and methodology, functional testing software also varies depending on the use case. Here are some of the options you’ll come across.
We couldn’t leave our brainchild out of the equation, could we? So the USP of TestSigma is simple! It works with web apps, mobile apps, and API tests. That’s helpful if you have your product on multiple tech stacks and are testing all of them.
Support for different operating systems and browsers is also present, along with the capability to provide continuous feedback. This capability is helpful, especially when you’re committing changes and want to check whether everything is working properly.
And given that it can efficiently run tests in parallel, devs can save a lot of time without waiting for threads to finish up. Then comes the insight, arguably one of the best things about automated tests. Everything was presented with easy-to-understand timelines and reports within short and long intervals.
Moreover, the overall integration of this suite (through the software development kit and APIs) seemed pretty smooth. That smoothness improved even more with the utilization of additional plugins like data generators from its extensions/add-ons collection.
No matter where you go, Selenium is a name you’re bound to hear for automated functional testing. This open-source program is primarily designed for web apps and has been around for a long time.
As expected, it supports most mainstream operating systems and browsers — from Linux, Windows, to Chrome and Edge (and the good-old IE — RIP). You can also write the tests in a bunch of languages, depending on your preference. The options include Java, Python, C#, PHP, and many more.
Creating tests on this is fairly intuitive, and the whole thing becomes much more effortless given how long it has been around.
This is a testing software developed by SmarBear, and it is rather powerful and versatile. For one, it comes with a prebuilt test editor for keyword-driven workloads. It supports languages like C#, JS, Python, and C++Script.
It covers a plethora of tests including unit testing, coverage testing, data-driven testing, regression testing, and many more. Another impressive bit is that it utilizes AI to recognize UI elements. Having such capabilities reduces the load on the tester.
What I like about this tool are its impressive continuous testing capabilities. Users can integrate it with all sorts of frameworks, ranging from Jenkins to Jira. And as expected, cross-browser testing is as good as it gets when it comes to testing web apps.
Certify is a robust testing software designed by Worksoft. It works flawlessly on multiple platforms and operating systems. The best thing is even large-scale software developers rely on it, meaning the scalability and capability of this tool are beyond doubt. Even large enterprise applications like Oracle utilize it.
The setup process and design are rather intuitive, and it supports all of the common testing methodologies. Moreover, this tool works exceedingly well if your testing team includes non-technical people in it. Users can write codeless tests with it, which allows everyone to play a role.
However, it may not be the ideal way to go for small-scale applications. The biggest reason behind that would be its price, which may not be feasible unless you have got a comparatively large-scale application.
Unified Functional Testing (AKA UFT)
UFT is one of the most popular tools utilized for functional test automation across multiple platforms and environments. This tool also features a quality object recognition capability, which reduces the load on your shoulders.
It works on a wide array of elements including APIs, mobile, desktop, and web apps and services. You could use this to run through the interface and both the service and database layers efficiently. This is not an open-source tool, so you’ll have to purchase the license.
The insights that it presented were easy to figure out and utilize to create swift decisions, cutting time off the testing process.
Here’s another GUI testing tool for the trio of desktop, mobile, and web applications. This is an excellent solution if you require continuous tests during your development cycle. That also means you’ll get quick feedback to get things running swiftly.
Another benefit of Ranorex is its intuitiveness. You can even take a no-code approach with this tool, which is especially beneficial for beginners. It also has support for all of the major platforms and browsers for cross-browser testing.
What this tool excels at is its object identification capability. For example, if you make changes to an element on the user interface, Ranorex will be able to update that repository accordingly without any intervention from the testers.
Moreover, it comes with a video reporting capability. That way, users can rewatch the whole test without actually running it. This certainly saves users time and resources.
Here’s yet another open-source automated testing framework. It’s primarily developed with Java and Swing. While Marathon is better suited for smaller applications, it has another version (MarathonITE) to work on larger and more complex programs. The environment is well integrated and makes test automation straightforward, even for beginners.
Another efficient tool for tasks like continuous testing would be Sauce Labs. What makes it unique is its cloud-based testing design, which gives you an edge for frequent tests.
It also comes with a low-code testing capability, meaning you can write the test script without any programming language. That’s pretty handy if you’re new to automation tests. Furthermore, the insights and reports are well-detailed and helpful. It also works with Selenium, Appium, and even Playwright.
There are some of the common tools used for automated functional testing. Of course, there are hundreds of them, but we don’t have all day.
How to Design the Automated Functional Tests?
So, now we want to design and automate a functional test for our program. There’s no ideal path to do that, per se. We have too many variables to create a one size fits all pathway. Nevertheless, here’s what I think is a good way to get started with automation testing.
Determining What to Test and Which Approach to Adopt
Be it a few people or a large QA team, the goal remains the same — making the software as functional as possible as efficiently as we can. To achieve that, the first step would be to determine which parts of the software or program you want to test.
You’ll sometimes find that not all of the bits need to be automated, especially if you’re only going to run them a handful of times. It makes more sense to run them manually in those cases instead of wasting time on automation.
Part of this decision will depend on which stage of development you’re in. For instance, you’d only focus on some specific functions if only a part of the program received changes. Moreover, it can depend on the type of functionality test you’re running (and many other variables).
Here are some methodologies that you might adopt:
Keyword-driven testing (some call it action word-based) essentially enables you to separate the test designing from test development. This is quite common with testing teams nowadays, where not all of the testers are adept at scripting languages.
If your QA team includes non-technical people, a keyword-driven test can be beneficial. These tests necessitate in-depth planning before you move on to the actual testing and development.
Data-driven testing relies on a set bunch of data from your database instead of some set codes. The best thing is that these don’t need anything other than your data and the test script to run, which makes test designing more convenient.
That means these can take less time compared to keyword-driven tests. These tests also reduce the possibility of errors. However, they are trickier to manage, especially for non-technical people.
As the name suggests, you can create a modular test script, where there are a bunch of automated testing scripts for the application. Testers can then set the overall execution pattern and have the test work as a whole. The best thing about these tests is that they’re far easier to maintain.
They also make it effortless when you’re working as a team on testing a program. It’s also possible to combine these modules to create different tests, and complex programs can benefit from them.
You can determine the suitable methods for your programs depending on your requirements. Then comes the question of choosing the testing tool.
Choosing an Automated Functionality Testing Tool
Once the path to follow has been chosen, the next step is to select the tool to create it. There are now hundreds of automated testing tools on the market, but only a handful would fully suit you. So, what should you consider in that process?
Firstly, determine whether your QA team is capable of coding the test scripts out. That won’t be the case for every team, and those teams may need to rely on keyword-driven testing. If you’ve only got coders as testers, then you have to look at which languages the tool supports, and so on.
Then comes the question of compatibility. For one, the language you’re using to write the software is crucial. The platform your software is designed for (e.g., for Android or browsers, or for Windows) is also essential to look at. Not every tool would be great at something like web applications or Android apps and vice versa.
Reusability of the code is another aspect to consider, as that can save a lot of energy. How it reacts to changes made in the UI or the test’s codebase is also a question we should ask. And if you need specific testing capabilities (let’s say Record & Playback tests), check if the tool can accommodate those proficiently.
The tool you choose will play a huge role throughout the automation testing journey, so it has to go right.
Defining What Counts as Successful
Each test has a standard that it would consider successful (also known as Definition of Done). Consequently, these standards are up to the testers to design and place. You could call an 8% success rate your definition of done, which would technically keep up with the standard.
That’s why it’s essential to write tests with the proper standards in mind. This is even more crucial when you’re working as a team. For example, maintaining a high accuracy rate or utilizing things like POPs (page object patterns) from the start should be a necessity.
Keeping the code clean and non-repetitive, ensuring the test fulfills all of the criteria before it calls it a day — things like these aren’t challenging or unreasonable. Therefore, we should ensure that they’re a requirement right from the start.
Writing the Automated Tests
Now, how we write the tests depends on a whole range of factors. The tool in use, the language we’re writing the tests in, the type of software we’re testing, the type of tests we’re scripting — the list does not end. And since none of us wants to stay here all day, let’s not go down that path.
However, there are some things you should remember while scripting for the best output. As an example, a data-driven test should be written to maintain its modularity. Modularity would allow for great things since the test itself can be used against different data sets without changing anything.
Another bit is writing the code (it could be a no-code tool as well) so that any changes in the UI won’t affect anything. Poorly-written tests often suffer because any minute change to the UI ends up bricking things. Iteratively scripting the tests is a great idea to keep things easy to fix and straightforward.
It’s also essential to keep the tests efficient so that you don’t spend ages on a single test. That’s not the ideal case scenario if we want to maintain a high ROI. Many people also overlook that unit tests can be easier to write and fix instead of complex stuff. Utilizing these while scripting can help the ROI and keep the people above happy.
My Two Cents at the End
Functional testing is a tough cookie to crack, and the logistics aren’t easy to maintain. So it’s only natural that the world has swiftly moved on to automated functional testing.
And I realize that it’s a vast arena — one that we can’t entirely cover in one article. Nevertheless, this piece should be a helpful stepping stone if you want to learn automation testing and figure out the right path to follow. Here’s to a functional and flawless program!