Can AI-Driven Test Automation Enhance Test Automation?
Software Testing has changed a lot!
Earlier, manual testing ruled the world of testing, however, test automation increasingly became a reality in most organizations developing software. Testing continued to evolve, and it took advantage of technology innovations.
Artificial Intelligence (AI) is one such technology that has made a substantial contribution to automation in general. But, can AI-driven test automation significantly enhance test automation?
Read on, as we explore the answer to this question.
Table Of Contents
- 1 Traditional test automation: What is it?
- 2 The growing importance of test automation
- 3 Artificial Intelligence (AI): A brief introduction
- 4 How can AI-driven test automation impact software testing
- 5 How AI-driven test automation enhances test automation
- 6 Test automation with AI: The need to embrace innovation
Traditional test automation: What is it?
Test automation, as we know it, is about using specialized tools to control test execution and comparing test results against expected results. Organizations use test automation to automate repetitive tasks, e.g., regression tests are common candidates for test automation.
Test automation represents a significant improvement from repetitive manual testing, where testers spend their time and effort to ensure that the software works as expected. Manual testers check log files, external services, and databases for errors, moreover, they record their findings.
Test automation uses tools for many of these tasks, which reduces time spent on exploratory tests and increases test coverage.
Automated testing tools automate several tasks, e.g:
- Regression tests
- Test data creation
- GUI interactions
- Logging defects
The common types of test automation frameworks are as follows:
- Data-driven frameworks
- Keyword-driven frameworks
- Modular automation frameworks
- Hybrid automation frameworks
Test automation tools can automate both functional as well as non-functional test cases. The following are a few examples of functional test automation tools:
- Quick Test Professional from HP
- Rational Robot from IBM
- Selenium, which is an open-source framework
Load Runner from HP and Jmeter from Apache are examples of non-functional test automation tools.
Here is a list of the top 20 functional test automation tools you need to try!
The growing importance of test automation
As more and more organizations have taken to Agile development, they are carrying out development and testing in multiple iterations.
The concepts of “Continuous Integration” (CI), “Continuous Development” (CD), and “Continuous Deployment” are very important in Agile.
Organizations can make continuous deployment a reality only if they continuously test their applications. As development proceeds at a fast pace, the testing must keep pace with it.
This makes test automation very important in the world of Agile development!
Artificial Intelligence (AI): A brief introduction
Artificial Intelligence (AI) is an interdisciplinary branch of computer science, parts of which have been commercialized. The objective of AI is to build intelligent machines that carry out tasks that would normally require human intervention.
AI achieves this by “learning” from large data sets, thanks to its algorithms.
Alan Turing, the noted British computer scientist had started the research on AI during WWII, and a long and eventful period of research and development followed.
There are four types of AI, which are as follows:
- Reactive machines: These machines have no concept of the past, and they can only focus on the limited task on hand, e.g., Deep Blue – IBM’s supercomputer that played chess.
- Limited Memory AI: These machines can recall events in the past, although to a limited extent. Self-driving cars use this AI.
- Theory of Mind AI: Research is still in progress on this kind of AI, and its objective is to understand the emotions and thoughts of people and other AI-powered machines.
- AI with self-awareness: This kind of AI is still in the early stage of research, and its objective is to create machines that can form representations about themselves.
There are multiple subfields under AI that serve different purposes, few of which are as below:
- Machine learning: Ability to automatically learn and improve from experience. Two main kinds include supervised and unsupervised learning algorithms.
- Deep learning: Uses multiple layers of neural networks that extract information progressively from the input.
- Cognitive computing: These systems or software mimic how humans think and help in improving human decision-making capabilities.
- Computer vision: Training computers to understand or interpret the visual world from images, videos and deep learning models.
- Natural language processing: Programming computers to analyze and process natural human language
AI has use cases in many key sectors like manufacturing, healthcare, etc. It’s a technology with transformative potential, and as mentioned here, Gartner forecasts that AI augmentation will create value worth $2.9 trillion in 2021.
How can AI-driven test automation impact software testing
AI can benefit the following areas of software testing:
- Unit testing: “Robotic Process Automation” (RPA) is an application of AI. Testers can use RPA tools to reduce flaky test cases while conducting unit testing, moreover, these tools help with the maintenance of unit test scripts. An example of such a tool is UIPath.
- API testing: You need to find the root cause of a bug when testing an application, and API testing makes it easier since you can get a deeper insight into the application. AI-powered tools can convert manual UI tests into automated API tests. This lowers the threshold of the skills needed for API testing, therefore, businesses can build a sustainable API testing strategy.
- AI/ML: AI/ML-based testing tools have become sophisticated enough to understand the intended use of an application. These tools do that by “learning” the application, and in the process, they gather enough useful data about the application. Subsequently, they use this data to create test cases where they show the expected results.
- UI testing: You can use AI-powered tools to automate your UI testing instead of doing it manually. AI-powered UI testing offers more accuracy in comparison with manual UI testing. Parameters like GUI size difference and combination of colours can not be detected easily using manual testing.
- Testing using visual validation: Image-based testing requires visual validation. Using the ML capability of AI, automated visual validation tools are making this kind of testing easier.
How AI-driven test automation enhances test automation
AI goes well beyond the scope of simple rule-based automation since AI/ML algorithms “train” AI systems using large datasets. This has helped AI to enhance test automation, which was earlier based on rule-based automation.
The following are a few examples of how AI-driven test automation enhances test automation:
AI-powered visual validation tools:
The key objective of image-based visual validation is to ensure that the UI appears correctly to the user. UI elements should appear in the right colour, position, size, etc., however, it’s very hard to sufficiently automate this kind of testing using rule-based automation.
Organizations often resort to manual testing for this, however, even manual testers can’t always detect discrepancies here. ML-powered visual validation tools help to automate these since ML can recognize patterns.
With APIs taking the centre stage in the world of application development, a significant part of testing effort focuses on the back-end. This boils down to API testing. ML algorithms provide testers with plenty of analytics, therefore, they find it easier to automate API testing.
Running more relevant tests
If you make a small change to your code, should you run the entire set of automated test cases? Is that relevant or should you run only a small subset of your test cases? With traditional test automation, you can’t answer this question.
However, AI/ML algorithms have superior analytics capabilities, therefore, you can find out what is the minimum set of test cases you should run to test a small change to the code. This improves your ability to run more relevant tests. You can use the same capabilities to analyze your test coverage and improve it.
Creating test cases
AI/ML algorithms can “read” your application and learn about it. These algorithms build a data set that contains observations about your application, including how its various features should behave given certain conditions.
This helps them to automatically create test cases where they record the expected results. Given that these tools “learn” using their algorithms, their ability to create test cases is far superior to what rule-based automation can achieve.
Test automation with AI: The need to embrace innovation
Test automation is a key capability in the age of Agile since it facilitates faster product iterations. Given their ability to “learn”, AI-powered test automation tools bring a level of automation on the table that simple rule-based automation can’t achieve.
Whether you are an IT leader in an enterprise or a tester, you should be open to embrace the innovation that AI has unleashed in the world of test automation.
Check out how Testsigma applies AI technologies to keep track of the changes, recommend the tests that are affected, fix (heal itself) and offer suggestions on a test failure.
See how the AI-driven test automation tool allows automated tests run unattended!