The focal point for any software development project is to execute proper operations to ensure error-free and bug-free software delivery every time. That’s where software testing comes into play! However, with a need for frequent software upgrades/updates, manual testing has taken a backseat. In contrast, automation testing plays a significant role in automating repetitive tasks and delivering high-quality software products.
AI (artificial intelligence) and ML (machine learning) are two cognitive technologies that come to mind when eliminating repetition and ensuring refinement. AI and ML are making a paradigm shift in software testing with deep learning-based algorithms. Today, various AI and ML-powered automation testing tools are available in the market. These tools transform the software testing processes and enable teams to deliver refined products.
How Are AI and ML Changing the Software Testing Process?
Any software testing process has three major stages: test case creation, test case analysis, and test case maintenance. Let us understand the contribution of AI and ML in software testing at various stages of testing.
Test case creation
Creating test cases for various scenarios is the biggest challenge that any software tester faces daily. That’s where AI and ML come to the rescue. These technologies allow you to write the cases manually while the system automates them. Automatic test case creation can allow you to identify and create test cases without any human intervention. You can apply this for visual testing and functional testing. For example, an automated test case creator can take design elements like input and create related test cases for validation purposes.
For this purpose, it can build ML models and use NLP (natural language processing) to understand the various test cases. Then, based on those learnings, it can create multiple test cases in the future whenever there’s an update/upgrade in the feature of products. Currently, the test case generation using AI and ML is in its infancy. With more research being carried out, the future looks bright and we will have more efficient and accurate methods for test case creation.
Test case analysis
In this area, you can leverage the power of ML and computer vision to conduct visual testing of your test cases. Based on this analysis, you can compare your current product version against previous versions and decide on any unexpected changes. By conducting an in-depth analysis of the resulting bugs/errors using AI and ML, you could provide QA engineers with high-risk areas of the source code. The developer would keep that in mind before deploying the applications and it will surely increase the reliability of your test cases.
Furthermore, one can use AI to create a link among bugs, test, and code commit that will eventually help you to identify the risk areas quickly. Also, ML models continuously observe and learn about your system and, based on these observations, provide excellent recommendations on how your site or application should perform. With these kinds of data at your disposal, mitigating the risk in future test cases would become easier than ever before.
Test case maintenance
Typically, manual testing requires teams to continuously monitor all test cases and make changes according to the current features of the system. The traditional test management and maintenance system relied on users to provide input from manual testing activities. Now, with AI And ML, you have automation testing in play where it will automatically push the results in test management and maintenance systems. The reason is that AI and ML-based testing practices rely on the self-learning model.
So, if there are any changes in the current system, it identifies them and changes the test cases on the go. As a result, it helps reduce your team’s workload significantly. There are some best practices for test case maintenance such as reviewing the documentation, updating old test cases, removing unnecessary test cases, and adding new test cases. Now, if you opt for manual testing in such scenarios, this task will become challenging. But, with AI and ML-based automation testing, you will have maintenance activities conducted at particular intervals without any human intervention.
Major Approaches for AI and ML Implementation in Software Testing
It is undeniable that AI and ML can help you at various stages of software testing. Now, let’s understand the four major approaches to AI and ML implementation in software testing:
1. Visual Testing
Visual testing also known as user interface testing is how developers can ensure that the user interface of a web or mobile app appears to end-users as per the original requirements. For that purpose, it conducts testing of the overall look and feels of a website or application with the help of AI and ML-based image processing and screen comparison algorithms.
Here are some of the popular visual testing tools:
- Applitools
- Percy
2. Differential Testing
Differential Testing is also known as differential buzzing is the technique to detect bugs in the software by applying the same input to similar applications and observing the differences. For that purpose, you can leverage AI and ML-based self-learning algorithms and find out code-related issues, security vulnerabilities, regressions, etc. It’s one of the new approaches for change detection.
Here are some of the popular differential testing tools:
- Laucnhable
- Google OSS-Fuzz
3. Self-Healing Automation
Self-healing test automation tools make use of AI and ML-based techniques to automatically update changes in the environment of an application. It checks the changes in UI and accordingly modifies the test cases. It also helps eliminate the need for test case maintenance. Here, the technology allows tests to adapt and automatically update and that’s why it’s known as self-healing.
Here are some of the popular self-healing automation tools:
- Mabl
- Testim
4. Declarative Testing
In declarative testing, the focus is on what to accomplish with this test rather than how the process will actually work. Identifying the intent of testing rather than determining how the actual testing process is important. Additionally, it enables you to verify the app’s final state vs. the expected state. The aim of this testing is to eliminate tedious, error-prone, repetitive tasks through automation thereby increasing productivity.
Here are some of the popular declarative testing tools:
- Tricentis
- UiPath Test Suite
Benefits of Integrating AI and ML in Software Testing
With the amalgamation of AI and ML in various phases of software testing such as test case creation, analysis, and maintenance, software testers can now sit back and relax. They don’t need to worry about continuously updating test cases and identifying defects. There are many other benefits that businesses can harness by effectively integrating AI and ML in the software testing process such as overcoming the limitation of manual testing, saving money, reducing defects, etc. Now, let’s focus on three core benefits and analyze them in detail.
- Increased test coverage: With AI and ML-based automation testing, you can increase the scope of your test cases. The more test cases you cover, the more in-depth testing is executed. As a result, you will be better positioned to deliver high-class products and increase the depth and scope of the test results.
- Faster time to market: With so much competition, you want to launch your products in the nick of time. However, if you opt for manual testing, you need to repeat the test cases when the source code changes. With the help of AI and ML-based testing, you can easily automate the task of repeating test cases execution without investing any time or money. As a result, you can reduce the software testing process to a few hours from multiple days, significantly fastening the time to market.
- Improved accuracy: Even the most experienced QA members can make mistakes while conducting software testing. Thankfully, AI and ML eliminate such possibilities. As we know, AI and ML algorithms can repeat the same process with utmost precision and accuracy based on their learnings. This level of accuracy is not likely with manual testing, as humans may miss out on some essential steps and may make technical errors.
Drawbacks of Integrating AI and ML in Software Testing
With benefits comes a set of challenges that you need to know about while utilizing any technology to its full potential. So, let’s analyze the various disadvantages of using AI and ML.
- Needs human intervention: Many organizations today support integrating AI and ML into their software testing ecosystem. Surely AI and ML will help you automate a similar task and execute it accurately. However, human intervention is required when you provide new input and want AI and ML models to learn the new trades. So, you can’t entirely depend on AI.
- Cost is a concern: You can choose from various AI and ML-based software testing tools available in the market. However, you may end up paying a hefty price to adopt them. If you’re someone with a limited budget or a budding startup, then cost may be a significant concern. It is one of the reasons why many organizations hesitate to use AI and ML in their processes.
- May compromise human intellect: There’s a fear among the IT community that over-reliance on AI and ML for software testing may compromise human intelligence. As a result, people may lose the motivation to work independently and fail to use innovative thinking as machines perform software testing procedures.
Conclusion: Future of AI and ML in software testing
We’re moving towards a time where continuous testing won’t be a new-age approach but the need of the hour. To meet such demands, opting for manual testing won’t be a viable option. That’s where automation comes into play and here, AI and ML will play a massive role. As we have seen, people are using basic forms of AI and ML. However, with more research being conducted in this area, you will see a definitive role of AI and ML in DevOps and CI/CD lifecycles. So, we would suggest you explore how AI and ML-based software testing can align within your ecosystem. We would also recommend you become familiar with the best practices for AI and ML in software testing. It will hold you in a good state for the next step of evolution.
https://www.toolbox.com/tech/artificial-intelligence/guest-article/exploring-the-role-of-ai-and-ml-in-the-evolution-of-software-testing/