The influence of artificial intelligence on our life is more and more significant, changing many industries and fields, affecting all aspects of daily life. Especially in the field of artificial process automation, artificial intelligence has played a prominent role. Therefore, this paper will study how artificial intelligence affects the field of software testing, especially in the field of automated testing.
What is software testing? Software testing is a process of evaluating the performance of the developed program to check whether it meets the requirements of customers, and detects whether there are errors before it is put into use, so as to improve it.
Every time a developer adds new code, new tests must be performed. Quality supervisors (QAS) spend a lot of time confirming that new code does not break the existing code base. However, manual regression testing is very time-consuming and will increase the burden of QAS to a large extent.
Traditional QA testing methods include checking a task list to ensure that the software works as expected. This is still a viable task when you only need to test a few features at first; but as the functionality increases, QAS faces the difficulty of completing full testing by the deadline. The more complex an application is, the more difficult it is to ensure that it has complete test coverage.
With the emergence of artificial intelligence, it is possible to simplify the testing process. In addition, new software and products must be released quickly under competitive pressure, and extending the testing cycle is not an intelligent move. As a result, there is no choice but to test smarter, not harder.
How can artificial intelligence overcome these challenges
The application of artificial intelligence in software testing tools focuses on simplifying software development lifecycle (SDLC). By applying reasoning, problem solving and machine learning, artificial intelligence can promote automation and reduce redundant tasks in the development and testing phases.
For example, suppose you are running an automation framework to test your project. Most of the time, you don’t know which tests to run, so you run all tests or a predetermined set of tests.
So what if an AI enabled robot could check current test status, recent code changes, code coverage, and other metrics, decide which tests to run, and then run them? Such software can effectively replace developers / testers in decision-making process.
Artificial intelligence and machine learning apply reasoning and problem solving from automated and improved testing. The use of AI in software testing helps reduce time-consuming manual testing, allowing teams to focus on more complex tasks, such as creating innovative features. The following are several aspects of using artificial intelligence to optimize software testing process:
Enhanced accuracy – we have to admit that even the most experienced quality inspection engineer can make mistakes. Due to the large amount of data, testers will inevitably be distracted and ignore some important defects. This is where AI and machine learning technologies work.
They teach systems to learn source analysis and apply knowledge in the future. In this way, AI tests produce more accurate results over and over again. Therefore, the use of artificial intelligence technology for data analysis reduces the probability of human error.
Speed up – instead of having to manually process thousands of code as testers do, AI quickly sorts log files, scans code in seconds and detects errors in less time. By incorporating AI into repetitive testing, quality inspection engineers can focus on testing new features or focus on important parts of the software.
More automated – AI programs can evolve with code changes. They can adapt and learn to recognize new features. When AI robots recognize a revision to the code, they can be programmed to determine whether it is a new feature or some defect in the code change.
Correctly understand customer requirements – in the software development process, the most important thing is to understand the actual needs of customers. AI can detect similar websites and applications to determine what factors can help win the target audience, and it can also help to study a large number of competitive products to determine their advantages. By correctly understanding the customer’s requirements, they can create test cases to ensure that the product will not be damaged in achieving these specific goals.
Current situation of software testing based on Artificial Intelligence
At present, autonomous and intelligent agents (called “test robots”) have been used to automate activities such as application discovery, modeling, test generation and fault detection, and machine learning technology is used to implement test robots, including but not limited to decision tree learning, neural networks and reinforcement learning.
Unlike traditional test automation tools and frameworks, machine learning allows test robots to run under uncertain conditions. The AI driven test methods developed in the past decade include the following examples:
Difference testing — compare application versions, classify differences, and learn from classified feedback.
Visual testing – use image-based learning and screen comparison to test the appearance of an application.
Declarative testing — clarify the intention of the test in natural language or domain specific language and let the system determine how to perform the test.
Self healing Automation – automatically corrects element selection in the test when the UI changes.
Since 2014, the number of suppliers providing AI driven testing services has increased dramatically. Most of these tool suppliers are start-ups, and they mainly focus on the system level testing of mobile applications, which has attracted extensive attention in the industry.
The easiest way to observe AI functions is to use various tools and frameworks. Here are some popular AI driven testing tools:
Will AI “steal” your work?
The current popular view on this issue is that artificial intelligence will not replace human beings, but will help them conduct QA testing. The main reason for this view is that the use of AI in software development is still in its infancy, and its autonomy level in more advanced fields is far below that seen, such as the field of automatic driving system, which is still dominated by human beings. But to be sure, the next step for AI to assist humans is to let AI master the whole process. It’s just a matter of time.
As we all know, the growth of technology, especially AI and machine learning, is exponential. It is not easy to predict the future AI capability. The day AI takes over the testing may come sooner than expected, so you need to be prepared.