Impact of Artificial Intelligence on Software Testing
AI has entered the life of every human. Virtual assistants, FaceID, chatbots, search engines, driverless cars - all these things as well as many others are powered by AI and taken for granted by most of us. Artificial intelligence has penetrated and is abundantly used in the most diverse industries. So it would be strange if opportunities to use it in software testing were not considered as well. As these days AI-powered software testing is indeed a hot topic in the QA community, let's have a closer look at the perspectives of artificial intelligence in this field.
A Brief Overview of Artificial Intelligence and Software Testing
To get an intimate understanding of the issue, we offer to start with defining the terms of software testing and artificial intelligence.
So, software testing is an integral stage of the software development process. It aims to explore and try out software in all possible ways to make sure that it works as expected and is reliable, secure, user-friendly, etc., in order to guarantee a great user experience.
Software testing is of great importance as product quality affects user acquisition and retention, ROI, reputation, competitive advantage, and others.
Comprehensive software testing can take a ton of time, effort, and resources, and as long as current market conditions require an extremely short time to market, there is a constant need to streamline and accelerate software testing as much as possible.
Artificial intelligence can be defined as an area of computer science enabling machines and programs without explicit instructions to carry out tasks usually requiring human intelligence, including speech recognition, visual perception, decision-making, etc. The key components of AI are ML or machine learning, neural networks, deep learning, natural language processing (NLP), computer vision, and cognitive computing. AI deals with data analysis, predictions, decision-making, etc.
AI algorithms can accurately replicate human intelligence, while ML enables systems to learn with no human assistance. Simply put, AI and ML can collect data and learn from it by revealing patterns for making the right decisions and predictions which can be useful for efficient software testing.
Why is there a need for AI in Software Testing?
Software testing has undergone significant changes over the years. In the very beginning exclusively manual testing was performed. Still, as it was very slow and labor-consuming, there was an urgent need for software testing optimization which gave rise to automation testing enabling faster test execution. Currently, we observe rapid adoption of continuous testing that suggests execution of automated tests in a product delivery pipeline with the aim to receive feedback on possible risks related to a release candidate as quickly as possible. Continuous testing gets the most out of automation testing to cope with the higher complexity and faster pace of modern software development and delivery. However, even now it is understood that very soon it will not be able to satisfy mounting needs for product delivery at the speed of light. Therefore, the future of software testing is often associated with the active use of AI.
You see, currently, automated tests are carried out automatically, but QA experts have to write and maintain scripts manually which is time-incentive and not cost-efficient. The use of AI in the software testing industry in terms of automated test case generation, test run, test selection, and test maintenance is really promising. The first steps have been already made, and some AI-powered test automation tools are available on the market, but they need to be improved. Anyway, there is no doubt that the main breakthroughs in this realm are ahead.
Prospects of Artificial Intelligence in the QA Field
It is doubtless that the integration of AI into software testing will boost smart automation testing and bring the QA industry into a new era. The most optimistic experts predict that AI-based software testing will be turned into an independent industry and replace QA engineers. The latter will play a new role by tuning AI-powered testing and monitoring its results. As the future of software testing is associated with the active use of AI, many far-seeing companies have already started investing in this industry. The following are the key expected contributions of AI to software testing in the near future:
Automated testing of requirements
There is every reason to believe that AI will play a crucial role in testing product requirements by means of predictive analysis based on checking other similar applications and solutions for a better understanding of what new features customers need.
Autogeneration of tests
The QA community has great expectations of AI in terms of test autogeneration.
Currently, attempts are undertaken to enable AI/ML-powered automation testing solutions to learn the business usage scenarios of AUT. They are supposed to learn an application automatically crawling and collecting valuable data like screenshots, page loading time, HTML pages, etc. As soon as enough amount of data is collected, ML can study it for expected patterns of the app behavior and generate tests which while being executed compare the current app work with familiar patterns. In the case of any defect, visual difference, slow response time, or similar issues, an AI-powered testing tool automatically identifies it as a possible issue. Still, in certain situations, the differences might be valid. That is why a human tester should be involved to validate this issue.
Optimized UI Testing
User interface testing is a field where AI has already shone. AI-powered UI testing tools analyze DOM and corresponding code to verify the properties of UI elements. Image recognition is also utilized for UI element validation.
In addition, such tools use exploratory testing to detect issues in UI and take screenshots for further checking by a human tester.
AI Assistance with API Testing
Traditional automated API testing is not an easy task as it requires clear API understanding and test writing for a wide range of scenarios to provide comprehensive test coverage.
Currently available API test automation tools detect API activities and traffic for further analysis and test creation. However, API test update is performed by human testers who have to check REST calls as well as numerous parameters thoroughly.
In the future, AI-powered API testing tools will be able to address this problem by checking traffic and revealing patterns and relations between multiple API calls, and combining them into sets by scenario. Besides, such tools will also analyze available tests to reveal interconnections between APIs, detect changes in them, and correspondingly update tests or generate new ones.
Automated Test Maintenance
AI-powered testing tools will be able to detect and assess modifications of code and update tests not aligning with them. This approach is very promising especially when it comes to simple code changes. In such a way modified UI elements or similar issues will not lead to test failure anymore.
AI-Powered Test Data Generation
Test data generation is one more prospective field for AI implementation. Machine learning can with ease generate data using available production data sets for learning.
Therefore, the generated in this way test data is almost the same as the production one which is perfect for efficient software testing.
Benefits of AI Integration into Software Testing Process
As we have already mentioned, AI implementation can be extremely helpful for software testing. So let's figure out in which ways.
The ability of AI to recognize patterns and images can be leveraged to find visual defects, i.e. check whether all the UI elements look and work properly by analyzing them at a pixel level.
Even the most experienced and professional human tester can make a mistake as nobody is perfect and there is always a possibility of a human factor. Automation testing helps to eliminate the risks of human errors. However, with the AI implementation in automation testing, it will be possible to reach even better efficiency and more accurate result recording. Therefore, AI can help to eliminate even the slightest chances of errors and enhance testing accuracy in general.
Customization of regression cycles
AI algorithms can detect and evaluate the recent code modifications, check current statuses of tests, and define an optimal test coverage required for app release. In such a way regression cycles can be properly adjusted to execute only the needed tests instead of running all available tests. Such an approach also can help to save much time.
More comprehensive test coverage
AI-powered automated testing can enable QA teams to enhance the software testing scope, as hundreds or even thousands of diverse tests can be carried out during each test run which leads to higher test coverage.
Besides, taking into account your business priorities and risk analysis, ML can ensure that your test coverage is expanded to a perfect extension.
Predictable release impact
With the help of neural networks, test history, and data on recent test results, AI can predict the impact of an upcoming release on end-users, i.e. whether the customer satisfaction rate will increase or decrease. With these insights, businesses can make corresponding adjustments to be confident about the success of the upcoming release.
Analysis of defect root causes
Manual testers pretty often face a problem of inability to reproduce a bug despite following the same plan of action. In such situations, the testers have to conduct a thorough analysis of the cause-and-effect relations of the issue. It can take much time and come to nought at the end of the day. In comparison, AI can cope with this task in a blink of an eye.
Faster product delivery
AI-powered tests can support continuous testing, which can result in much faster releases that are the main aim for most businesses. Besides, as long as AI has all chances to automate not only test execution but test case generation and test maintenance, it can significantly speed up the overall QA process.
Test script writing and maintenance are rather time-incentive and hit one in the pocket. AI can optimize these processes and make them budget-friendly. Moreover, integration of AI into software testing results in early and quick bug detection, which not only improves product quality and makes it more reliable for end-users but lets product owners save money as the earlier a bug is found, the cheaper its fixing.
Flexible testing process
Even a slight change in code can lead to a test failure. The modern software testing approach is somehow rigid, as test scripts have to be maintained manually in order to keep up with app evolvement and modification. ML and AI algorithms will identify any app changes and adapt tests to them automatically in real-time. In such a way, the whole testing process will become more flexible and faster.
Objective test results
When it comes to manual testing, numerous grudges and debates between a QA team and a development team on test deliveries are the norm. AI-powered automated testing can yield more accurate and objective outcomes helping to avoid such human interactions that may result in delays in the testing procedure.
Artificial Intelligence can take software testing to the next level by covering all testing phases - test planning, test execution, and reporting, and what is important - without human involvement and in a timely manner.
The first steps have been already made in this direction and the basic forms of AI have already been introduced in automation testing tools, but there is a need to continue the software testing evolution to gain the efficiency needed to meet ever-growing market demands.