What are the Advantages of AI in Testing?
Taking into account the brilliant opportunities and abilities of AI, the most diverse industries consider its implementation as extremely beneficial for their optimization and development. QA experts also associate the future of software testing with the active use of AI. Some steps have already been taken in this direction but currently available tools must be significantly improved and optimized in order to unleash the potential of AI to the full and achieve the most ambitious goals. So, let's try to figure out together why a combination of AI and QA can be beneficial and why there is a need for this.
Is Artificial Intelligence and Software Testing a perfect duo?
Since ages ago time has been considered to be the most precious thing. These days this is as relevant as never. The pace of modern life is extremely fast. We have already automated numerous processes and revealed ourselves from the most various tasks but we still suffer from the lack of time. There is a constant need to accelerate different processes and procedures. For software testing, time is of great essence. As technologies are developing, the applications are getting more sophisticated. It means that the scope of testing is constantly growing while time dedicated to software testing is gradually being reduced. As a result, software testing should get smarter, more speedy and efficient. Currently, great hope is put on artificial intelligence as the best way to boost automation testing and meet market demands.
Since modern AI systems have abilities associated with human intelligence like planning, problem-solving, self-learning, reasoning, prediction, manipulation, etc., we have a chance to switch from continuous testing to completely autonomous testing in the near future which will bring mind-blowing changes in the QA industry. So, our answer is “yes” - AI and software testing is the perfect duo.
Reasons to Introduce AI in the Software Testing Process
Software testing techniques have been revolutionized over the years. The first manual testers could not even dream about the ways software is verified today. But there is still a necessity to keep on going and reach new heights. Present-day industry leaders understand this clearly and make certain attempts to optimize QA processes by introducing AI that can address the necessity:
To accelerate software testing as much as possible
Modern automation testing is faster than manual testing but it is crystally clear that it will not be able to meet the ever-growing demands of the market as for product delivery within an extremely short period of time. AI is able to tackle the issue through autonomous and fast software testing.
To get maximally accurate test results
A human is an apex of creation. But we all are non-perfect and even the best experts make mistakes from time to time. AI in these terms is more reliable and AI-embedded testing tools will give an opportunity to eliminate human factors and receive accurate test results. The more data is collected and fed to deep learning models for training, the more accuracy you can expect.
To extend test coverage to the maximum providing indeed comprehensive testing
One of the main software testing principles states that exhausting testing is impossible as it requires a ton of time and resources. However, the aim of a QA team is to reach as high test coverage as possible. Taking into account the great opportunities of AI, there is no doubt that with its active and proper use it will be indeed possible to check all the ins and outs of an application under test.
To reveal root causes of bugs in a timely manner
Every tester knows that it is not enough to detect a bug, but to reproduce it, and reveal its root causes. In reality, it is not always possible. Certain bugs can show up just under particular conditions that a human cannot recognize at once. AI in its turn will be able to conduct a thorough analysis and provide information on bug root causes in the blink of an eye.
To cut down testing expenses
According to stats software testing expenses can amount to 40-50% of a total project cost. As long as software testing is an essential part of software development, it is impossible to cut corners on it. So all efforts are being made to optimize it and reduce its price. With autonomous and fast software testing fulfilled by means of AI, there is a possibility to make QA services more budget-friendly.
To provide more efficient software testing
Since AI-powered testing tools can provide better accuracy and higher test coverage it is not surprising that they will help considerably enhance software testing efficiency in general and deliver software products of extremely high quality in record-breaking time.
The Most Prospective AI Applications in Software Testing
AI can have a broad range of applications to address the most various challenges of software testing. Below you can see the most prospective areas for AI use where certain results have already been achieved.
AI algorithms can check the current test statuses, identify recent code modifications, code coverage, and some other metrics. With this information, AI-powered test solutions can efficiently pick up proper tests to be run. In such a way, thorough data analysis enables AI to replace a QA engineer in some decision-making processes.
AI can be actively used in API testing as it can recognize patterns and interconnections between varied API calls during UI exercising. On the ground of this analysis, an AI-powered testing tool can provide a set of API calls with corresponding parameters for testing. AI can provide appropriate patterns and inputs for API testing by thoroughly analyzing end-users' behavior. Created in this way tests can address more complicated cases providing a high quality level of the API.
Unit testing is the first candidate for AI-enabled automation testing as such testing does not suggest any interactions with other app components. AI can carry out a static analysis to detect parts of the code that have not been covered with unit tests yet. Having this information in place, AI-powered testing tools can create needed unit tests with corresponding inputs. However, such tools should be good at defining code sections that require stubs and mocks to get properly separated. To succeed with this, the tool has to undergo comprehensive training that requires plenty of time to let the AI system understand the way unit tests must be created. As soon as the AI tool can properly deal with mock and stub creation, it will help a development team save a great deal of effort and time.
AI has found an active use in UI testing due to pattern and image recognition. These properties of AI are used in different automated visual validation tools that verify UI by checking UI elements in terms of size, color, shape, and position on a page. Such tools check elements on the pixel level and therefore can detect visual defects that human eyes cannot catch.
Test Data Generation
Quite often the efficiency of software testing depends on the quality of testing data. Real-world data collection is an expensive and time-incentive process. Besides, in most cases, such data cannot be used for testing or training due to privacy requirements.
AI models use deep neural networks and certain privacy logic to generate a great amount of synthetic data that is highly realistic and complying with global data protection standards (e.g. GDPR, CCPA).
What is the future of AI in Software Testing?
The future of AI in the software testing industry is indeed promising. The basic AI forms have been already used in some automation testing tools. But the main goal is to get the most out of artificial intelligence and make completely autonomous testing possible. Just imagine, that your AI-powered software testing tool provides totally automated test planning, test writing, test performance, test maintenance, and test reporting making the whole software testing process self-controlled, self-customized, flexible, accurate, reliable, fast, and efficient. This is the future of software testing that will surely come into reality by means of advanced AI-enabled testing tools.