AI In Software Testing: Full Guide For 2024

Quality Assurance or Software testing - a core step in the development process of any product. With its help, programmers see if the applications work as planned and are error-free. In the past, quality assurance engineers manually checked the software products or made scripts automated tests. Artificial intelligence technology offers a new approach to this process.

Combining AI and software testing lets businesses effectively check the quality of solutions. However, there are several things to consider regarding this practice, such as the variety of methods and optimal circumstances. Our guide covers all this and more, allowing programmers to decide better where to use AI software testing.

Using AI For Software Testing: Top Statistics

In recent years businesses have used AI technologies for analytics, automation, or generative purposes. While these applications are a significant factor in the growing funding for AI solutions, programming assistance also becomes one. Here are just some of the latest statistics we came across:

• Forbes experts predict that between 2023 and 2030, the use of AI for software testing will increase by 37,3%.
• A World Quality Report 2018-2019 by Capgemini reveals that 57% of businesses experiment using AI and ML for testing and QA purposes.
• Another study by Capgemini shows that using AI in software testing helps reduce test design and execution efforts by 30%.
• According to IDC.com, by 2025, 40% of the central IT budget will be spent on AI applications for different types of software testing.

Main Differences Between AI and Traditional Software Testing

Traditional software testing is a tedious and time-consuming process, it is still really needed for any digital product but the biggest issue is that it makes QA engineers manually design and implement evaluation procedures for different parts of custom software development solutions. 

Usually, this process can take two to three weeks to complete depending on the project's complexity. Of course, creating automated scripts for some components is possible, but they also require adjustments.

Introducing artificial intelligence to software testing makes it more reliable, efficient, and effective if you follow the right approach. With its help, enterprises can safely automate many repetitive and mundane tasks. In addition to handling many operations, this technology helps identify and predict software defects. It makes testing more thorough and accurate.

By leveraging software testing with AI tools, companies can accelerate the testing cycle while improving the quality of their products. AI can analyze vast amounts of data and predict areas that are likely to fail, reducing the chances of human error and overlooked issues. This not only speeds up the process but also ensures that potential problems are detected early, allowing teams to make adjustments before deployment. Ultimately, AI's ability to adapt and learn from patterns makes it an indispensable tool for more accurate and comprehensive software testing.

Methods Of Using Artificial Intelligence In Software Testing

Software developers and dev teams can explore three main methods of AI testing. They help speed up this process and make it more accurate and adaptable.

• Defect Analysis and Scheduling. AI-based defect analysis helps software engineers predict problematic parts of the code. It prioritizes test cases based on the chance of defects, allowing programmers to focus on these critical parts. This method ensures efficient and optimized testing efforts for crucial aspects of software solutions.

• Regression Automation. AI helps programmers establish if the old features haven’t changed after introducing new ones. The regression automation method uses this technology to run intelligent tests triggered by code changes. Introducing artificial intelligence in this application helps speed up the test cycle and makes regression testing more accurate, robust, and less time-consuming.

• Self-Healing Automation. This method utilizes artificial intelligence to update test scripts, saving time on manual adjustment automatically. It’s an excellent choice for software products whose code changes frequently and breaks the test scripts. Self-healing automation allows for keeping them updated and accurate, reducing maintenance efforts.

The Advantages Of AI In Software Testing

Artificial intelligence use in programming has more benefits than cost reduction and network simplification. AI developers and businesses deliver more robust and dependable tools thanks to several advantages of combining AI and software testing for their products. Let’s have a look at other advantages.

1. Better meet client needs. AI-based solutions help software engineers comprehend and follow customer specifications. This technology compares related websites and establishes their appeal factor to the target audience. With the help of artificial intelligence, it’s also possible to analyze similar software products.

2. Broader test coverage. Using AI for testing expands the scope and complexity of quality control. This enhances software solutions and makes them more optimized and secure. This is achieved by assessing internal program statistics, file data, and memory use.

3. Faster tests. Artificial intelligence produces detailed scans of log files in seconds. This lightning speed helps the technology discover errors and flaws faster than it takes software engineers. The advantage of AI in software testing is that it delivers more accurate bug findings without human mistakes. While it runs automated checkups, QA experts focus on checking out new features.

4. Enhanced automation. Utilizing AI to run regular tests saves considerable time and effort. With its help, developers don’t have to worry about introducing new code for QA to check. Thanks to enhanced analytical capabilities, this technology finds flaws and the latest functions in the code without human guidance.

5. Improved regression tests. Artificial intelligence in software testing speeds up and improves the quality of regression tests. Businesses use ML-based tools to make robust test scripts and look for any clashes in user interface updates. Additionally, they are perfect for validating modifications that are hard to test manually.

6. Lower costs. Companies and developers firms significantly cut testing expenses thanks to AI. Testing systems use the tech for regular evaluations without additional costs. This also enables enterprises to plan their development expenses more accurately.

7. Use of visual tests. Artificial intelligence solutions are a potent tool for visual web page validation and the evaluation of UI content, such as load times and element layout. Some complex tasks associated with this process can be done through automated tests.

Despite the versatility and apparent benefits of AI QA testing, there are times when this technology can and can’t help with the evaluation of software products. Let’s dive deeper into suitable and unsuitable cases for AI testing.

Suitable Tasks For Using Artificial Intelligence In Testing

• Automation of similar workflows. Once introduced to one workflow, AI tools learn how to automate similar flows quickly.
• Executing test cases based on changes. Artificial intelligence conducts risk analysis and decides which test cases to execute after finding changed areas in the code. This ensures that nothing will break in the app before its release.
• Maintenance of tests during code changes. The technology allows for the quick fixing of test cases broken by changes in the code.
• Making test cases for one field. AI software has to understand and automate the test cases that must be run for one field type only.
• Performance and load speed tests. With the help of AI QA testing, programmers automate tests for performance and load speed.
• Pre-release tests. AI tools help decide which tests to run before releases based on new features and changes.
• Test planning. AI aids in planning test cases for new features and how to execute them.
• UI testing. AI helps easily create test cases that test user interface workflows instead.

Unsuitable Tasks For Using Artificial Intelligence In Testing

• Documentation review. AI can’t understand the structure and components of a software product the same way software engineers do. So, this point can be done only by a professional QA team. 
• Tests development for complicated scenarios. Using artificial intelligence in testing is unsuitable for scenarios that require many components. Experienced QA testers are much better in these circumstances.
• Test result reports and analysis. Artificial intelligence also needs help comprehending test outcomes and deciding which steps to take next.
• UX design testing. The technology can’t evaluate the user experience of software products since it’s not a human being. In this case, we recommend calling for UI/UX experts. 

Traditional Software Testing vs AI Software Testing

Regular QA and AI-based software testing are two approaches to the same process, with advantages and drawbacks. Here’s how these methods compare to each other:

Manual Testing:

1. Time-consuming, and resource-demanding.
2. Running tests with QA engineers is longer as these experts have to perform linear and sequential evaluations.
3. Humans execute test cases manually, which involves a lot of active evaluations.
4. Low productivity.
5. There’s no guarantee that tests are 100% accurate, as there are always chances for human error, especially in complex cases.
6. There is no way for testers to consider all possible scenarios, resulting in less test coverage.
7. The high costs of parallel testing are due to the need for human resources and time.
8. Additional expenses must be spent on hiring and training QA engineers.

AI Testing:

1. AI-based tests save time and funds, leading to faster product development.
2. Accelerated test execution thanks to automated processes.
3. Test cases are automated with minimal human intervention.
4. Higher productivity.
5. Better test accuracy compared to manual evaluation due to automated monitoring of all activities.
6. More comprehensive test coverage as AI executes more of them quicker.
7. Support parallel tests, lower resource use and costs since they can be run on the cloud.
8. This process requires investment in AI tool training with higher future cost-effectiveness.

Leveraging AI in Software Automation 

Implementing artificial intelligence in software testing opens new, effective ways of addressing recurring issues. This helps organizations create tests faster and more efficiently.  In turn, they have a higher chance of meeting user and engineer expectations through automated tests. 

They ensure the effectiveness of software products by addressing several challenges:

1. Traditional test automation requires Python engineers who must possess a versatile mix of skills and experience. They can have trouble making practical automated tests and setting up test frameworks. AI mitigates these risks by offering high-quality, reliable tests that check many aspects of software solutions.

2. Usual QA automation frameworks are another challenge that comes from manual testing. Basic automated tests require some knowledge of the software’s implementation details. In many cases, tests are made from the developer’s point of view instead of the user’s.

3. Lastly, regular automaton tests have low readability and need to be simplified. Additionally, programmers rarely reevaluate them to check if they’re accurate, practical, and relevant. This can lead to irrelevant and outdated tests, making the evaluation process inefficient.

Factors To Consider When Implementing AI Testing

Using artificial intelligence to test software products is a potent tool that enhances the effectiveness of this process. With that said, there are several things enterprises must consider when implementing these technologies in testing.

• Select the right tasks. Not all test types can be handled with AI. This technology better suits tasks that require vast data analysis, finding patterns, and decision-making. But, it has trouble handling tests that require a user-first approach.

• Consider data quantity and quality. To run successful tests, companies must train AI models using Node or Python to recognize errors in code structure. The quality and size of this information will directly influence the effectiveness of AI-based tests.

• Set goals and interpretation results. The enterprise must clearly understand the tasks artificial intelligence must achieve and how to evaluate its results. Setting the wrong goals leads to errors or undesirable results.

• Integration with the current process. Viewing AI as a component of the testing process, not its replacement, is vital. Tools using this technology must be carefully integrated into existing processes and practices.

• Use a collaborative approach. To understand the main advantages of AI in software testing, companies have to establish a synchronized team of programmers and QA testers. Their expertise can help maximize the efforts from its implementation.

Conclusion

Using AI in software testing helps developers speed up the programming process and ensure the proper work of solutions post-release. Although it’s unsuitable for some QA tasks, implementing this technology helps save companies funds and free up testers. Companies that adopt it will have a competitive edge in the quality and ongoing support of finished products.

Finally, we need to point out that AI testing always needs manual QA engineers' support as no AI tool can perform 100% without the services provided by humans and companies.