The world is changing faster than ever. And now artificial intelligence is a common thing in the work and life of thousands of people. AI is a robust, smart, customizer way to deliver software products to consumers faster.
The primary goal of AI is to cope with information that only humans can process, such as visual information, speech recognition, patterns, object recognition, the ability to learn from past experience.
Such a large and rapidly growing field as software testing is implementing AI into a testing process too. According to the World Quality Report 2019-2020, 21% of CEOs of software testing companies claim that their companies are putting AI trials. And 16% of respondents already use AI in test activities.
How can AI help software testing? Why is it important? What kind of benefit software testers can retrieve from AI? And what are the biggest challenges?
Why is AI in software testing important?
Computer scientists are working on the improvement of AI, and with each year, it becomes smarter. Notwithstanding scientists' hard work AI isn't smart enough to replace humans and can't work independently from AI specialists. However, AI can be a great helper to facilitate work, reduce risks and failures. Automation testing is a robust method for reducing risks and facilitating testing activity, but it has limitations. And AI is perfect for eliminating these boundaries and making automated testing more efficient.
How is AI improving software testing?
Expands test coverage
AI can't replace humans, but it can make the testing process faster and more convenient. When programmers develop and add new features and functions, the amount of code lines grows, so it's hard to track down possible failures manually. AI checks thousands of code lines in seconds and finds failures faster than testers. Besides, AI can develop and adapt to changes in code. AI can be programmed to distinguish failures and new functions in a system. When programmers make code changes, it can lead to failing tests as a system can't find the elements it needs to interact with, but AI can identify which locator to use to detect elements.
Algorithms AI can choose paths in complex systems to identify failures. Simultaneously, AI learns on received data and identifies new user journeys inside the system to find potential bugs. It increases the coverage and performance of a testing activity.
AI doesn't make mistakes associated with human factors and doesn't get tired. AI can gather and sort a large amount of data, fast and correctly. These factors help software testers to concentrate on other specific parts of testing activity and deliver products faster.
Better automation testing
The main goal of automation testing is to speed up a testing process and reduce time-consuming. But there are some fields where automation testing can't cope with. For example, usability or functional testing that needs human perception and mind. But as we said above, AI focuses on tasks that only humans can cope with. Of course, now AI can't perceive and process information from the external world like humans, but AI has some achievements in this field. Some AI tools for software testing can provide automated visual testing. Visual testing helps to ensure that every UI element looks correct, and an interface hasn't visual mistakes. AI can identify failures in colors or shapes of elements that humans may miss.
Predicts failures and bugs
A testing process isn't a one-time process. Testing is always a repeating cycle to identify new bugs or how code changes affect the system overall.
Continuous testing is a part of automation testing to identify risks and provide quick feedback. Implementing AI algorithms in this process allows testers to observe results and detect regularity or tendency. Also, AI can provide data for analytic with possible bottlenecks inside the system. According to this data, testers can eliminate risk before a failure happens.
Writes test cases
Spidering AI is a new approach that can write test cases or its part by crawling code and learning on gathered data. This method helps to save time and understand what part of the code to test.
AI algorithms scan code, collects data and creates a dataset with patterns. When something critical happens, AI compares the existing patterns and the new one. After this, AI reveals problems and alerts about it. Software testers analyze these alerts and can decide which failures are eliminated.
Challenges with AI in software testing
Although AI is a smart system, it is created by humans. Hence, AI can make mistakes too. And a big problem of AI is a high level of trust in its algorithms. Implicit faith in AI algorithms can lead to missed failures and risks. Lack of knowledge and experience of testers leads to wrong data interpretations and the inability to identify where AI algorithms mislead engineers. Another problem is selecting a type of testing for AI algorithms and incomprehension of what test cases should undergo with AI. Test engineers run all test cases or some set without understanding what they will get as a result.
Hence another challenge — lack of skills and knowledge among test engineers. Companies that intend to implement AI into testing activity should educate employees or pay attention to specialists with knowledge of deep learning principles and applying generic modeling tools to testing.
Sum up
Software development changes drastically. Artificial intelligence will change the whole software industry to the better side. But does this mean that software testers disappear? No. The tester profession will reshape to new rules. It will become more strategic. Software engineers will focus on key testing goals, leaving routine work for AI.
AI is a perfect partner for DevOps, and together these two approaches can deliver products faster, increase profit and conversion. Hence, more and more companies will apply AI technology.
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