In the early days of software development, testing and development were executed in a linear fashion. With time, the traditional waterfall model had to give way to the agile methodology since it increases collaboration and generates better results on the execution front.
Agile testing has become an absolute essential in today’s times where testing and development go hand-in-hand. Not only that, continuous testing is integral to any software product release since it helps in running automated tests as a part of the delivery pipeline.
Faster developer feedback and accelerated product delivery are some of the must haves in today’s uber competitive market. All of this can only be achieved if the software testing process is able to unearth bugs at a faster pace. The automated testing framework (or tool) being used must be able to detect test flakiness, thereby helping improve the overall product quality.
The inclusion of Artificial Intelligence (AI) and Machine Learning (ML) in the testing domain can be the answer to reduced test flakiness, faster test failures (for quicker feedback), and increased test maintenance & reliability. As per reports, the global AI market size is expected to surpass US$ 1,597.1 billion by 2030.
Like all the other domains, AI is having a positive impact on the software testing industry. In this blog of the AI in testing series, we look at the integral elements of AI in software testing. In the latter sections of the blog, we would cover the benefits offered by AI as far as creation and execution of test scenarios is concerned.
Evolution of AI in Software Testing
The software industry has advanced significantly over the last few years. As per the World Quality Report (2019 ~ 20); Artificial Intelligence (AI), Machine Learning (ML), self-healing automation, automated security testing, and intelligent test analytics are some of the top trends in software testing.
Since AI has the ability of mimicking human intelligence, the penetration of AI in testing is on the rise. AI combined with ML algorithms learns from the previous test run data. It extracts patterns from the data for deriving important decisions that help in expediting further test runs.
You can expect less test flakiness, improved test stability, and increased ROI (Return on Investment); that too without any human intervention. In case you are looking to leverage the benefits offered by AI in testing, it is recommended to onboard an experienced AI Testing Company so that tests can be planned and executed at scale.
Benefits of AI in Software Testing
Now that we have answered the ‘why’ of AI in software testing, let us look at the primary benefits of AI in software testing.
1. Reduced Test Flakiness
Software tests whose results are not deterministic are considered to be flaky. Consider a test suite whose test cases were passing in iteration (1) but a couple of tests start failing in iteration (2). The test cases whose outcome is not the same (even after no code changes) are flaky tests.
Test flakiness could be due to bad test case design, shortcomings in the test infrastructure, external factors that compromise with the test results, and issues with the newly written code (if any). Flakiness in tests can create a huge headache to the testing (or QA) teams.
AI in conjunction with ML algorithms can be instrumental in reducing and effectively managing flaky tests.
2. Better Test Coverage
As a tester, you would agree that 100% test coverage is a myth. You should be realistic when setting the ideal test coverage percentage since there would be a good number of tests (e.g. exploratory tests) that must still be performed manually.
Though test automation frameworks and tools like Selenium, Cypress, etc. help in achieving better test coverage; the coverage can be further improved by using AI in automated testing. There are a number of AI-driven automated testing tools that aid in improving the test coverage.
These tools are designed to learn from the test runs, data quality; thereby helping accelerate automated testing and improve the metrics of test coverage.
3. Faster Feedback Time
The intelligence factor in AI plays a critical role in software testing. AI helps in faster detection of bugs in the product. Early detection results in improved product quality since the developers receive faster feedback about the product.
Accelerated feedback time also improves developer productivity since issues are reported at a faster pace. The impact of AI-based testing multiplies by a huge margin when the tests are run in a CI (Continuous Integration) pipeline.
4. Faster Test Script Generation
As mentioned earlier, AI in the tool learns about the various aspects of the tests (i.e. test case quality, data about the past test runs, and more). The learning aspects of AI helps in faster and more effective test generation. Test generation is expected to be more breezy with AI factor in software testing.
Past project inputs and past project runs are the food for thought for the generation of newer test scenarios. All of this learning is also instrumental in generation of tests in an automated manner.
Algorithms based on AI and ML can be trained for detecting minimal changes in the application issues. All of this results in minimal human intervention for updating the test scripts.
Also Read: Transformation of Software Testing with AI
Types of AI-based Test Automation
Till now, you would have realized about the immense potential offered by AI and ML in software testing. Many organizations that do not have in-house expertise in software testing onboard a AI testing services company like KiwiQA so that they can accelerate product release by leveraging the benefits of AI.
Here are some of the various methods of AI-based test automation:
Prediction and Analysis of Defects
With automated testing tools based on AI, you can expect a more accurate prediction of the flaws in the AUT (Application Under Test). Since the tools have all the required data (past and present) at their disposal, accurate prediction is realized at a speedier pace.
Automation of Regression Test Suites
Creation and maintenance of regression test suites can be a tedious task, especially if the logic and/or UI/UX changes are being done more frequently.
The inclusion of AI in testing makes regression suite maintenance a less difficult job. Existing regression test suites can be intelligently updated depending on the gravity of the code changes.
The AI capability in automated testing tools can be extensively used for reducing the testing cycles since it can orchestrate test case (or test suite) execution in a more efficient manner.
It is a given that the various aspects of the product (e.g. features, UI, UX, user flow, etc.) would undergo changes from time to time. Changes in the product features might also result in updates in the test code.
Test maintenance can become a huge bottleneck since the tests have to keep up pace with the changes in the code. This is where self healing of automated tests can be highly beneficial.
In self-healing automation, machine learning algorithms play a key role in the maintenance of the test cases by recognizing changes in the AUT and adapting the test code to accommodate those changes.
Artificial Intelligence (AI) is playing a pivotal role in the software industry which also includes software testing. Though automated software testing tools are useful in developing and running tests at scale, maintenance and flakiness still remains a problem.
This is where AI and ML in testing can help in minimizing the test flakiness, improving test stability, reducing efforts involved in test maintenance, and realizing faster feedback loops. Software companies with minimal in-house testing expertise can benefit from AI in testing by partnering with an experienced and proven AI testing company like KiwiQA. Such an association can help in reducing the time to market by a huge margin!