Trust, but test: The importance of AI in QA

Artificial intelligence adoption in business has become a new trend with 72% of organizations using it for at least one function. When it comes to software quality assurance, AI is also a good assistant for that. 65% of surveyed IT companies have already implemented this technology into their QA processes.

Do you want to know how to use AI in software testing, and what benefits and challenges it is associated with?

A quick overview of QA's evolving history

Before we get into the details of how AI and QA are connected, let's first make a brief excursion into the history of testing. Below, we will take a look at how this area of IT has emerged and improved over time.

The advent of testing

Testing as a separate branch of IT appeared in the 1950s after the Handbook for Quality Control by Joseph Juran was published. At that time, specialists paid thorough attention to exhaustive testing: the comprehensive checking of all software components.

Two opposing testing formats

Over time, as the software became more advanced and complex, the exhaustive variant of quality checking has become impossible. And in the 1970s, testing took a slightly different form. In this “era,” there were two ways of quality check.

These two opposed models complemented each other. At that time, there were only manual tests, so the quality control process was long and labor-intensive.

Automation as a step forward

The first breakthrough in this field occurred in the mid-1980s when the first automated tests appeared. At that time they were quite primitive, without the possibility of creating scenarios in scripting languages, but they still helped to make testing quicker and easier.

Adding flexibility and improving testing tools

The next leap in the evolution of testing occurred in the 1990s when it took the form of quality assurance that resembled the modern one. At that time, agile testing became popular. In this model, quality control was carried out not only after development was completed, but also at intermediate stages of working on software creation. That's also when more advanced QA tools started to appear, for instance:

This made the quality control process quicker and more effective. Although there is still the need for a human IT specialist to create automated tests, it takes much less time than conducting manual testing.

Implementing AI in QA

After the advent of AI, QA testing reached a new level again. The first AI-powered tools for software quality control appeared in the mid-2010s. Since then, they have constantly evolved and new ones have emerged.

When it comes to modern QA, AI is often used for it in the following ways:

The role of AI in automation testing: Associated benefits and complexities

AI is becoming more and more prevalent and is being adopted across all industries. Between 2015 and 2019 alone, this technology experienced a 270% increase in popularity among businesses, according to Gartner. IT companies are implementing this technology to:

In particular, they use AI/ML-based tools to improve software testing as well. When companies start using AI, quality assurance becomes faster, more efficient, and less effort-taking. 43% of respondents stated that adopting this technology helped to significantly increase the productivity of their QA teams, according to the latest surveys.

What other benefits are there to using AI tools in testing? And what challenges can be encountered on the way to implementing this technology? Find the answers to these questions in this article.

4 essential advantages of AI-powered QA tools

54% of companies consider using AI to be useful for IT process automation, according to the study by PricewaterhouseCoopers. What are the advantages of this technology when it comes to software testing?

Speeding up testing cycles

Quality assurance, like the entire software development process, is usually divided into cycles. AI can help significantly reduce the time spent on each separate cycle and the whole testing process. It can be used on each of the 6 testing phases described below.

1. Analyzing requirements

To determine what exactly needs to be tested, QA specialists study the requirements and specifications. These include details about:

ML/AI algorithms can analyze the list of requirements and specifications and suggest ways to check whether the software meets all of them.

2. Planning the testing process

At this stage, the specialists develop a comprehensive strategy. It includes the details about:

AI/ML-based tools can help to:

Creating test cases

At this stage, QA engineers develop test cases, each of which contains information about:

Automated scripts for each test case are also developed at this stage.

Within this cycle of QA, AI can help determine what kind of test cases and how many of them are needed for effective testing. Also, generative AI tools can assist specialists in writing, improving, and adjusting automated scripts more quickly.

3. Setting up the test environment

In this phase, engineers prepare test environments that meet QA objectives. At this specific step, AI can assist in arranging and deploying test environments used to verify the correctness of software operations.

4. Executing tests

In this phase, tests are run to check how certain features work or what properties the application shows in a particular environment. AI-driven automated testing tools can enable the execution of hundreds of tests during every run, speeding up this cycle. Also, this step can be improved with AI by creating self-healing scripts that will adjust to code changes and updates to keep functional all the time.

5. Reporting and analyzing results

After the tests were run, the specialists drew up reports on their results. At the end of the tests, specialists prepare reports on their results. This is very important to:

AI-based QA tools can help generate reports faster and thus spend less time on routine work. Also, they assist in enhancing the process of analyzing the results for constant improvements.

Accelerating each QA cycle allows businesses to significantly reduce the time to market. This, in turn, contributes to gaining a competitive advantage.

Finding bugs more quickly

With AI tools, it is possible to speed up the defect detection process. There are many ways to do this, for instance:

Generating practical ideas and reports

AI can bring new ideas to your QA workflow. When engineers have been working on the same project for a long time, their eyes may glaze over. Because of this, they may miss some of the shortcomings. Artificial intelligence, on the other hand, never gets tired or burned out.

Generative AI tools are always ready to suggest new ideas on how to improve testing processes to make them more efficient and faster. Also, they can become good assistants in creating and maintaining testing documentation. With their help, it is possible to make reports faster and easier.

Saving costs

When companies implement AI, quality assurance becomes more cost-efficient. The use of such tools significantly speeds up and optimizes processes. It means that engineers can do greater scope of tasks in the same number of working hours. Thus, the business gets better results for the same money.

Also, artificial intelligence allows organizations to hire fewer engineers to do more complex and creative tasks instead of hiring a large number of specialists to do routine work. It saves money on the maintenance of the team.

Moreover, using automated processes to minimize manual tasks contributes to the prevention of professional burnout. This means decreasing the turnover rate in the organization. This gives an opportunity to save on HR costs because the company will not have to constantly search for, hire, and onboard new team members.

Challenges of integrating AI in QA testing

There are many considerable benefits to using these latest technologies in quality assurance. But why are some companies still hesitating whether it’s worth implementing them? Here are some challenges that make businesses unclear about the worthiness of AI usage.

Data issues

According to recent surveys, this is one of the most common reasons why organizations are afraid to adopt AI. 26% of companies cited this as the main challenge that prevents them from incorporating this technology into their processes.

Using such tools requires sharing a lot of information about their projects with AI. A lot of this data may be confidential. This is why many companies are not certain about the appropriateness of AI usage. They are unsure of the sufficient security of the information they will be giving these tools access to.

Furthermore, to effectively use AI in QA, you need to have very well-defined, detailed, and bias-free information about the product. Some companies don't have enough comprehensive documentation, so AI tools will not be able to provide them with sufficient results.

Lack of method understanding

Many AI/ML-driven tool creators do not disclose the details of their products’ operating principles, such as:

Because of this, many companies do not trust AI and do not consider this technology reliable. 76% of interviewed CEOs stated that they are concerned about the lack of transparency in the AI field.

Frequent changes

The field of artificial intelligence is developing by leaps and bounds. The tools are constantly receiving updates and upgrades, and the principles of their operation can change extremely often.

Due to this, many businesses are hesitant to incorporate such unstable technologies into their processes. They don't know what to expect tomorrow from the tools used today, whether they will function in the same way and produce the same results.

Cultural resistance inside the team

Your employees may be against the use of these technologies for several reasons, for instance:

To avoid problems related to cultural resistance, prepare your team of QA for AI usage in advance. Here are a few ideas of what you can do for this:

Final thoughts and predictions about AI for software testing

Artificial intelligence is rapidly evolving and penetrating all areas of life. It is already being used by 72% of companies to improve their various processes, including IT and QA in particular. 97% of those who have already implemented these technologies in their testing report an increase in productivity to some extent (43% —significantly and 54% — somewhat).

Implementing this kind of innovation in QA has such advantages as:

However, nothing is perfect, and the adoption of this technology can have some drawbacks that stop businesses from using it. These are, for instance:

Although AI/ML technologies are still evolving and still have room to grow, they are already being used actively and effectively by many companies to improve their testing processes. Such tools can become great assistants that make the work of QA engineers more productive.

And their prevalence will only increase. The global market of AI is expected to exceed 1800 billion dollars by 2030, growing by more than 36% each year, according to the latest research.

Contact DeviQA, the expert in AI-powered QA, to learn how to boost your testing with the latest technologies!