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 AI testing 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 AI in testing.
Is Artificial Intelligence and Software Testing a perfect duo?
Since ancient times, time has been considered the most precious asset. Today, this is as relevant as ever. The pace of modern life is extremely fast. We have automated numerous processes and freed ourselves from various tasks, yet we still suffer from a lack of time. There is a constant need to accelerate different processes and procedures. In software testing, time is of great essence. As technologies develop, applications are becoming more sophisticated. This means that the scope of testing is constantly growing, while the time dedicated to software testing is gradually being reduced. As a result, software testing must become smarter, faster, and more efficient. Currently, great hope is placed on artificial intelligence as the best way to boost automation testing and meet market demands.
Since modern AI systems possess abilities associated with human intelligence, such as planning, problem-solving, self-learning, reasoning, prediction, and manipulation, we have a chance to transition from continuous testing to completely autonomous testing in the near future, which will bring mind-blowing changes to the QA industry. So, our answer is “yes” - AI and software testing are the perfect duo. Moreover, the future of quality assurance lies in artificial intelligence testing.
Reasons to Introduce AI in the Software Testing Process
Software testing techniques have been revolutionized over the years. The first manual testers could not have even dreamed about the ways software is verified today. However, there is still a necessity to keep progressing and reach new heights. Present-day industry leaders understand this clearly and make concerted efforts to optimize QA processes by introducing AI, which can address the necessity to accelerate software testing as much as possible.
Modern automation testing is faster than manual testing, but it is crystal clear that it will not be able to meet the ever-growing demands of the market for product delivery within an extremely short period of time. AI is capable of tackling this issue through autonomous and rapid software testing.
To get maximally accurate test results
Humans are considered the apex of creation. However, we are all imperfect, and even the best experts make mistakes from time to time. In these terms, AI is more reliable, and AI-embedded testing tools offer an opportunity to eliminate human factors and achieve accurate test results. The more data that is collected and fed into deep learning models for training, the greater the accuracy you can expect.
To extend test coverage to the maximum providing indeed comprehensive testing
One of the main principles of software testing states that exhaustive testing is impossible, as it requires a significant amount of time and resources. However, the aim of a QA team is to achieve as high test coverage as possible. Considering the great opportunities offered by AI testing, there is no doubt that with its active and proper use, it will indeed be possible to thoroughly examine all aspects 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; they must also reproduce it and reveal its root causes. In reality, this is not always possible. Certain bugs may appear only under specific conditions that a human might not recognize immediately. AI, on the other hand, can conduct a thorough analysis and provide information on the root causes of bugs in the blink of an eye.
To cut down testing expenses
According to statistics, software testing expenses can account for 40-50% of a total project's cost. Given that software testing is an essential part of software development, it is impossible to cut corners on it. Therefore, all efforts are being made to optimize it and reduce its cost. With autonomous and fast software testing conducted through 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 overall 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 various challenges of software testing. Below, you can see the most promising areas for AI use where significant results have already been achieved.
Decision-making
AI algorithms can check the current test statuses, identify recent code modifications, code coverage, and other metrics. With this information, AI-powered test solutions can efficiently select the appropriate tests to run. In this way, thorough data analysis enables AI to replace a QA engineer in some decision-making processes.
API Testing
AI can be actively used in API testing, as it can recognize patterns and interconnections between various API calls during UI exercises. Based on this analysis, an AI-powered testing tool can generate a set of API calls with corresponding parameters for testing. By thoroughly analyzing end-users' behavior, AI can provide appropriate patterns and inputs for API testing. Tests created in this manner can address more complicated cases, ensuring a high quality level of the API.
Unit Testing
Unit testing is a prime candidate for AI-enabled automation testing, as such testing does not involve interactions with other app components. AI can perform a static analysis to detect parts of the code that have not yet been covered by unit tests. With this information, AI-powered testing tools can generate the necessary unit tests with corresponding inputs. However, these tools must be adept at identifying code sections that require stubs and mocks for proper isolation. To achieve this, the tool must undergo comprehensive training, requiring a significant amount of time to enable the AI system to understand how unit tests should be created. Once the AI tool can competently handle mock and stub creation, it will help a development team save a considerable amount of effort and time.
UI Testing
AI has found active use in UI testing due to its capabilities in pattern and image recognition. These properties of AI are utilized in various automated visual validation tools that verify the UI by checking UI elements in terms of size, color, shape, and position on a page. Such tools examine elements at the pixel level and, therefore, can detect visual defects that human eyes might not catch.
Test Data Generation
Quite often, the efficiency of software testing depends on the quality of the testing data. Collecting real-world data is an expensive and time-intensive process. Moreover, in most cases, such data cannot be used for testing or training due to privacy requirements.
AI models employ deep neural networks and specific privacy logic to generate a large amount of synthetic data that is highly realistic and complies with global data protection standards (e.g., GDPR, CCPA).
What is the future of AI in Software Testing?
The future of artificial intelligence in testing is indeed promising. Basic forms of AI have already been utilized in some automation testing tools. However, the main goal is to maximize the potential of artificial intelligence and make completely autonomous testing a reality. Imagine an AI-powered software testing tool that provides totally automated test planning, test writing, test execution, test maintenance, and test reporting, making the entire software testing process self-controlled, self-customized, flexible, accurate, reliable, fast, and efficient. This is the future of software testing that will surely become a reality through advanced AI testing tools.
