An Introduction to AI-Powered Test Automation
As AI is taking software testing to its more efficient, cost-effective and reliable era, here is our ultimate guide to AI testing.
Test automation has been in development and used in software testing for more than three decades. The solutions we have now are a far cry from the bulky, unwieldy tools that the QA industry had at its disposal in the beginning.
We have indeed come a long way, with better capabilities, features, integrations, and robust frameworks. With the evolution of test automation tools, the entire structure of QA methodology has also changed over time, coming on from the waterfall model to the world of DevOps and Agile development that pervades today.
With the advent of AI or Artificial Intelligence, the QA industry is poised to evolve into the next phase of software testing methodology, with AI-powered test automation promising to be the solution that takes modern test automation platforms and workflows to the next level in terms of efficiency, reliability, and cost-effectiveness. With the power of AI and machine learning, many of the long-standing pain points of test automation are getting addressed, new functionality is coming to light, and QA teams are getting the confidence to involve AI-powered test automation into their workflows more and more.
Automation emerged as a solution for the rigors of manual testing decades ago. However, from the start, the infusion of automation in the QA process, while solving many problems, also gave rise to some new ones. It became an entirely new segment of the QA workflow that takes a lot of time and effort. In the early days, test automation would face serious issues with identifying testing scenarios that were a perfect match for the application of automation, coding the actual tests, and then maintaining the tests over time to account for changes in the software.
With AI, these problems can be a thing of the past. So far, enterprising businesses have been able to implement AI in test automation solutions to generate new test cases, generate datasets, analyze test results, and transform existing tests. AI can, therefore, make your test automation significantly smarter, open the doors towards more potential applications of test automation, and even help you save time and money by cutting process time and cost significantly. In fact, using AI-powered test automation might be a way to mitigate test debt, something that QA teams routinely worry about.
Using AI-powered test automation tools, QA teams have the opportunity to move from partial or conditional automation to high levels of automation and, finally, full automation with confidence. According to recent statistical data, QA teams have been able to dramatically improve the testing process, create better quality software, and handle large and complex testing requirements, even those that involve large, distributed IT systems.
As real-world use cases have shown, AI-powered test automation tools are currently being used by organizations all over the world for diverse use cases, including detecting and forecasting mobile app crashes, creating static program reviews, forecasting test coverage requirements, automatically generating tests, harnessing AI and ML to handle product recalls, forecasting and monitoring system observability, and even searching for more esoteric error codes.
To understand AI-powered test automation, its salient features, and its many benefits, we need to start by looking at test automation in general, its inherent problems and issues, and how the application of AI can enhance the process of test automation itself. In this guide, we will look at the gradual evolution of legacy test automation, the pitfalls of legacy test automation, and the unique properties of AI-powered test automation tools that can help solve most of those problems. We will conclude with an analysis of the benefits of AI-powered test automation tools and why you need such a resource for your organization.
The Basics of AI Test Automation
First, we need to look at the inherent qualities of test automation as it has been since its inception so that we can understand the challenges that come with it. While test automation practices and tools have indeed evolved over time to accommodate more testing scenarios and QA workflows, there are still certain limitations that QA teams have to contend with. These challenges make their presence felt during specific parts of the QA lifecycle and can be clearly categorized depending on their impact.
The Evolution of Test Automation
With a top-down view of the evolution of test automation, it is easy to see how AI-powered test automation can be considered the logical next step of the evolution and part of the same chain. Test automation, which started as simple batch scripts with very little practical capability, has undergone many changes in the past decades.
Initially, test automation was confined to simple batch scripts and shell scripts that could trigger certain commands and achieve a single, limited purpose. Then came the capability to record and play back tests, which could be used to speed up simple, repeatable tests. From there, testing tools started to incorporate a variety of verification tools and techniques like setting up checkpoints to further refine the process and compare actual test results with expected outcomes.
This gave to reusable test frameworks, where tests could be modular. Here, tests would be broken down into smaller, simpler functions that could be used repeatedly in many different types of testing scenarios. Tests could also be altered or transformed completely by changing, removing, or adding other functions. This dramatically reduced the effort it took QA teams to create and more importantly, maintain test scripts.
With the advent of parametrization, test automation become more data-driven and intuitive. This enabled the same test to be used with multiple sets of data, mirroring very plausible and practical use cases. The next phase of evolution was a major one, bringing continuous testing and in-cycle automation to QA. This helped the process of creating, running, and maintaining automated tests a lot more self-sufficient and reliable.
These tests were usually created for unattended execution and designed in the same sprint cycle. A common use case would be an integrated CI/CD pipeline where a build would be deployed and the deployment would trigger a plethora of automated smoke tests. The code would need to pass those tests to finally reach the production environment.
With that, we come to the era of cognitive QA and AI-powered test automation. The goal here is to create test platforms that are intelligent and need even less human participation or interference. These systems use AI to make the entire process more intuitive and can help massively with keeping tests properly maintained.
A Short Summary of Legacy Test Automation Issues
In the planning phase, for most legacy automation tools, the test scripts are created using code. This means that you need programmers with a high level of proficiency in writing code to create your test scripts. The more complex the test, the more complex the coding that is required to create the tests. If you want to set up a new team quickly to start the planning phase for a new QA project, getting the right team members with the requisite programming skills can be the first and one of the most difficult hurdles.
A perspicacious way to overcome this hurdle is to use partially or fully codeless test automation tools so that team members with little to no programming skills can start creating and implementing tests quickly and immediately, dramatically reducing your time and effort invested in this endeavor.
Another major problem that you need to solve at the very outset is choosing the right automation tool for your particular use case. The market is full of different test automation tools, each with its pros and cons. At the planning phase of your project, it can be quite a challenge to test out multiple test automation tools to figure out which would work the best with your particular use case. Instead of adapting your project to your test automation tool, you can be much more productive if your test automation tool adapts with your use case. AI-powered test automation can make use of data models and outcomes from previous projects to train itself to serve your purpose better.
When it comes to authoring the tests, you can run into a different set of challenges. With every new project, the QA team can be bogged down by the number of repetitive tasks that need to happen every day. While some test code might be reusable in certain contexts, most days are usually spent writing new code so that the testing can commence and move forward. For example, for each new sprint of a web automation project, the QA team has to create new data models and different page classes. This is an immense chore and can quickly take up so much time and effort that you already enter test debt.
However, the bulk of the challenge in test automation comes in the test maintenance stage. With the way legacy test automation works, most tests would need to be heavily maintained and, in some cases, completely rewritten, even for small changes to the code. For starters, every time a test fails, it can take valuable time to find out whether the root cause is an actual issue with the software or a badly written test case. Small code changes can cause automated tests to fail entirely, with the failures sometimes being entirely random. If the QA team has to repeatedly check the results of a test automation run manually, most of the benefit you get from the automation evaporates right there.
To understand the seriousness of this issue, imagine a minor UI change in the software you are testing. While the change might be so minute as not to be even noticeable, this will certainly fail an automated test that relies on a script. A single line of different code can cause multiple tests to fail and hold up your workflow for several hours or even days. Therefore, QA teams would need to spend more time manually evaluating test results and having to maintain and transform test scripts for even a minimum level of code change.
Furthermore, QA teams also have to evaluate and come to an efficient decision on how much retesting or regression testing is required for every change in the software to maintain high standards of quality while also making efficient use of the time and resources at hand. Being more thorough is more reliable, but costs more in terms of time and money. Being selective is usually the modus operandi of QA teams in this scenario.
A modern test automation platform that can help the QA team figure out the optimum test cases for every change using AI can be a major help. As applications grow larger and more complex, it is also a major issue for QA teams to properly maintain a repository of multiple test scripts and suites. Unless this is done with a pre-devised plan that goes into deep detail, this can be a mammoth task. A smart and intelligent system can make short work of this and save QA teams significant time.
Understanding AI or Artificial Intelligence
To appreciate the true worth of AI-powered test automation, it is crucial to first understand what AI is and its inherent characteristic properties that can lend themselves well to the field of software testing. AI or Artificial Intelligence is, in short, creating capabilities in machines that allow them to function in ways similar to a human brain. With AI, machines can be trained to develop skills like problem-solving and learning. When you think of AI, you might automatically think of very advanced computers or robots from the future. In reality, AI is much more ubiquitous.
In today’s world, any machine that can fully or partially perceive its surroundings and make informed decisions based on its training and perception to achieve a particular goal can be termed artificially intelligent. A lot of this comes from the notion of machine learning. Machine learning is a field where machines are taught to behave or respond in ways that do not require explicit programming. This means that machines can learn, both from data provided to them and their perception of their surroundings, to consistently improve their capabilities over time.
Machine learning has already found major use in both unsupervised and supervised learning scenarios. With random and unlabeled data, it is possible to design a training algorithm for a machine so that it interprets the data, finding relationships and patterns. Over time, its ability to interpret this data also becomes more superior as it “learns” more. Moreover, with data already labeled by humans, similar or better results can be achieved sooner by creating the right labeled datasets, choosing the appropriate training algorithm, and running multiple iterations based on results.
The Role of AI in Test Automation
A close look at the issues inherent with legacy test automation can provide an accurate idea regarding the role AI can play in test automation. When test automation tools become more intelligent through the use of AI, QA workflows can become faster, more economical, and more conducive to creating higher-quality end products.
In a nutshell, the requirement for experienced and skilled coders to create test scripts can be mitigated with a codeless test automation tool that can intelligently adapt to multiple use cases. The rigor of repetitive tasks when planning and starting a new project can be helped by a test automation tool that can automatically and intelligently generate a skeleton testing plan, replete with data models and page classes, using its learning from previous data.
The issue of flaky tests and minor UI changes wreaking havoc on test results can be addressed with a test automation platform that can intelligently identify page elements and uses AI algorithms for smart and dynamic define locators for web elements. Severe test script maintenance issues, a common contributor to test debt, can be cured by a test automation platform that is intelligent enough to self-heal the test scripts. Finally, the troubles of creating a regression test suite that strikes that perfect balance between coverage and efficiency can be helped with a test automation tool that intelligently analyzes several important metrics to create a smart regression workflow.
Here is a more in-depth look at how AI can help make test automation tools better while reducing cost and effort.
Test maintenance is inarguably one of the major pain points of legacy test automation. Due to the fragile nature of these tests, the outcomes can be unexpected even after a small change to the code. An updated object property, like CSS or Xpath, can immediately break a test automation script. Therefore, these object changes need to be manually identified and updated in the object repository. This takes up significant time and effort and can contribute to test debt.
The solution is self-healing tests. With AI-powered test automation, all of this can be solved by using a workflow that can automatically detect these changes, sort them out dynamically, and make sure that test automation scripts keep functioning as intended despite these changes. With a dynamic location strategy, the very nature of test automation can be enhanced dramatically. With the help of AI, the test automation tool can automatically detect any change in the object identifiers in a particular web page without the need for human intervention. It can locate the element, dynamically change the requisite attributes, and ensure that the identifier is automatically updated to reflect the change.
With intelligent self-healing, QA testers can save a lot in terms of effort and time. It also helps create a workflow that is more in tune with the shift-left approach preferred in the Agile development methodology, and more conducive to efficient operation, increased quality, and faster delivery.
Intelligent Test Generation
Another pain point in legacy test automation is the creation of test scripts, which takes a lot of time and very specialized, skilled effort. Programmers have historically used languages like Python, Java, and Ruby. With intelligent test generation, most of this effort can be mitigated and tests can even be designed by QA testers with limited programming skills.
With AI-powered test automation, QA teams can, at the very outset, use the framework to outline a set of functional requirements that can govern decision-making later in the chain. In fact, the AI element can also make use of tests written in plain language by using a natural language processing algorithm to convert them into actual code. This can cut down the time it takes to design test scripts drastically.
In legacy test automation, even when test scripts would be written with reusability in mind, there would be a fair amount of compromise in the effectiveness of such an approach. With AI-powered test automation, repetitive tasks can truly be left to the automation framework. AI algorithms can make this process easier by intelligently writing page classes and creating data models.
With the page object model, a page class effectively discerns individual internal components of a page, including web elements and the necessary actions associated with them. Then, this is turned into functional flows that test scripts can call upon. The whole idea is to keep the verification element separate from the actual page operations. However, this method necessitates creating page classes for every single page of the application. This is a huge coding effort that takes time and resources.
Using AI, however, it is possible to create page classes intelligently by scanning individual pages and reverse-engineering that information to generate unique page classes. While this might not always create page classes that are immediately ready for use, QA testers can get a very close approximation that requires little or no tweaking before use.
Similarly, AI can also be used to create data models that represent different kinds of test data for web forms and other elements. With the smart creation of data models using historical data, QA teams can very quickly arrive at a skeleton test framework in the planning phase and can immediately progress into authoring actual tests.
In visual testing, the visual appearance of a particular page is compared with an expected baseline image of the page. In legacy test automation, this comparison would be carried out using a pixel-by-pixel scan of both images. This is also known as snapshot testing, where a snapshot of a particular page would be compared pixel-by-pixel with a reference image. This would often result in unreliable results as small changes in the UI or other parameters like display resolutions could cause these tests to break. In legacy systems, this kind of testing would usually generate a lot of false positives.
With the use of AI in modern test automation tools, pages can be scanned in a manner that replicates the vision of the human eye. With automated visual testing that leverages AI, tests can easily distinguish between a visually acceptable difference and a mismatch that indicates an actual defect. This is especially relevant in visual testing, where changes in resolutions, browser configurations, and a lot of other factors can result in mismatches. With artificial intelligence, there is no need to create assertions on individual elements of the page. The testing can rely entirely on the visual appearance of the full page. Instead of a pixel-by-pixel comparison, the test automation tool would carry out an intelligent comparison.
Smart Regression Testing
For QA teams, the topic of regression testing has always been a particular pain point in terms of legacy test automation platforms. The question is to ascertain the right number of minimum test cases to ensure optimum quality and reliability while keeping the number of tests to a minimum to save time and remain efficient. Teams can either choose tests based entirely on their experience with the application that is being tested or pick the regression tests that have been already categorized according to the core functional areas of the project. Common outcomes would include bugs getting leaked into releases or significant delays in the software development pipeline.
With artificial intelligence, these issues can be rectified to a great degree. AI-powered test automation tools can process and derive insight from very large volumes of data and can therefore take into account an unprecedented number of factors and metrics while making intelligent decisions about balanced regression testing requirements. With the help of detailed training over time, the AI algorithm would be able to create the optimum size of the regression test suite for any change in the application. This way, QA teams can reach the most balanced approach with aggression testing purely based on dependable data.
For example, with the right AI-powered test automation tool, QA teams will be able to create a regression testing suite that is based purely on the potential of impacted functionality. The automation platform can intelligently identify all the test cases connected to a particular functional area with the potential to be impacted by the change under test. Similarly, the automation tool can also help identify tests that fail with a greater frequency, which can then be analyzed based on prior test reports and historical data.
It is even possible to use the past records of individual developers or development teams for writing buggy code as an important metric in the creation of regression testing suites. Using these multiple parameters and data points, QA teams can arrive at an extremely optimized and streamlined regression testing suite that eliminates any needless testing while making sure that quality is maintained throughout the process.
Why You Need AI-Powered Test Automation
In this day and age, AI-powered test automation has become a growing need for QA teams on multiple counts. This is a sure way for teams still achieving only partial or conditional levels of test automation to progress to a high level of automation and eventually move on to a fully automated testing workflow. With AI-powered test automation, QA takes less time, costs less, requires less effort, and yields significantly better quality of results.
Here is a fairly complete list of all the broad benefits you can get from AI-powered test automation, not limited to further developments in the field and new opportunities arising down the line.
- AI-powered test automation tools can leverage their intelligence to preclude the need to write code to create tests. This brings the ease of use typically associated with low-code or no-code solutions to the test creation process. Even non-technical QA testers with little or no programming knowledge would be able to use these tools to create tests, significantly speeding up the software delivery process and removing the need to hire skilled and experienced programmers.
- With AI-powered test automation, it becomes a lot easier to increase test coverage, something that is crucial for preserving high levels of quality in the end product. With smart regression testing, QA teams would be able to create optimum testing scenarios that do not compromise with quality while also keeping the testing workflow streamlined and efficient. With full automation covering multiple testing areas, you can also easily incorporate more tests into your workflow without losing time or effort, or elevating costs.
- Similarly, you can achieve a greater degree of accuracy with your tests. When QA teams have to spend less time manually going over the results of automated tests, they can devote more time to other important areas of the project that determine accuracy and quality.
- With AI-powered test automation, you can go above and beyond the capabilities of manual testing and legacy test automation solution. This can help you save time, money, and effort. More importantly, it can also open up new workflow opportunities and bring about a transformation in overall philosophy and best practices.
- With test automation tools powered by AI, you have more flexibility in your testing. As these tools can adapt to different situations, requirements, and use cases, you have the opportunity to automate a larger chunk of your overall testing requirement.
- If you want to achieve a faster time to market, AI-powered test automation can be your friend. With the significant time and effort you would be saving by adopting such a solution, your software can get to market faster without any of the compromises typically associated with such a move. This has very important implications for a software development company.
Here are some compelling features and functionality that you can enjoy with AI-powered test automation tools.
- Low-code or no-code operation for test creation and automation.
- Automatic and smart generation of test scripts and test cases using ML and AI.
- UI tests that are more stable and complete quicker.
- Self-healing scripts that dramatically reduce your maintenance workload.
- Increased test coverage.
- Visual identification of problems in the software.
- A significant reduction in your test cycle times thanks to AI.
- Intelligent regression testing based on insight gathered from historical data.
- Intelligent logging of traces for more efficient handling of the test flow and failure points.
- Prediction of potential defects based on Big Data.
- Better availability of execution metrics and maintenance of execution history.
- Intelligent QA dashboards that offer real insight.
- Real-time test diagnostics and test insight.
As the technology progresses and gets more refined and AI algorithms get smarter and more capable, AI-powered test automation tools will provide even greater utility. In the future, you might be able to leverage predictive analysis using high volumes of Big Data to predict potential points of failure in software, based on its code structure. It is also possible to leverage AI to study common user behavior and patterns during regular usage of an application and integrate that behavioral data in your testing methodology to create priority test cases.
AI-powered test automation tools can also help with test recommendation, using historical data to recommend individual tests and larger test suites based on the developmental changes of the application. Since these solutions include machine learning and leverage Big Data, you might be further aided in streamlining your QA operation by making use of continuous learning algorithms which study real production data and create tests based on its learning. The AI element can even help you create and schedule tests based on non-traditional parameters that can impact certain projects, like a historical analysis of page response and rendering times.
The Outlook is Clear
Test automation is a crucial part of most software development projects, with more complex and time-consuming projects standing to get a great benefit from automating more tests. The market also dictates demand for faster delivery with zero compromises on quality. QA teams of this day and age need to carry out thorough, extensive end-to-end testing for every product in a very limited frame of time. It is always a challenge to create test cases and scripts that can fully cover all the critical areas of the software.
Test automation tools that leverage AI and machine learning have gained popularity due to their effectiveness in handling the inherent issues of legacy test automation platforms. All over the world, software companies are allocating larger budgets and resources to leverage AI-powered test automation platforms. The market also dictates the need for these specialized tools.
As a QA engineer, this is a golden time to learn more about AI-powered test automation and join the bandwagon. These tools will surely become more and more integral to the testing process of most companies in the near future.