Boosting Software Testing With AI

Artificial intelligence (AI) operates based on algorithms that analyze data, recognize patterns and learn from previous experiences, which enables it to improve the efficiency and accuracy of its answers. Applications using this technology involve speech recognition, natural language understanding, image recognition, strategic games, autonomous vehicles and Big Data processing.

Currently, many AI-driven solutions incorporate large language models (LLMs). LLMs, like GPT-4, are advanced AI systems using natural language processing (NLP) and trained on massive text datasets to understand, generate and translate text. Using transformer architecture, they analyze word context within sentences to produce coherent and meaningful responses. Trained on billions of examples, LLMs learn grammar, semantics and relationships between words but operate based on patterns rather than true comprehension.

While businesses increasingly look into implementing AI into their products and services, AI technologies are also often applied to other processes – for example, software development and testing. Read this article to find out how software testers can leverage AI, what tools they can use and how AI impacts their work.

Using AI-based tools in software testing

The use of AI in the daily work of a software tester is a chance to reduce repetitive tasks and focus on more complex problems. With the support of intelligent tools, software testers can focus on the essence of testing. This impact can be compared to the Industrial Revolution, where machines took over many heavy, manual tasks, enabling people to focus on more creative and strategic activities. Today, AI can take over repetitive aspects of testing, giving human experts more space for development, innovation and deeper problem analysis.

AI-based tools for manual testers

Though their work doesn’t directly involve automation, manual testers can still benefit from AI tools, which they can use to quickly analyze and interpret data and organize their work more effectively.

AI-powered test automation platforms

Tools of this type enable you to create and execute tests without having to write code – similarly to Selenium IDE. These platforms use AI to identify changes in applications and automatically adapt tests, which significantly saves time on their maintenance. With these solutions, testers can focus on more important aspects of testing instead of updating outdated tests. Examples of AI-powered test automation platforms include Mabl, Testim and SmartBear.

It’s worth emphasizing that using such tools can help manual testers expand their automation skills. By learning best practices like creating loops, reusing them in other tests and designing parallel tests, manual testers can get more familiar with key automation techniques that will prove useful in their next automation projects.

AI for log and data analysis

AI can quickly process and categorize substantial amounts of data, which is why it can be great support for manual testers in analyzing logs from systems and applications. Tools such as Loggly, Sentry or Datadog can automatically detect anomalies and provide reports, enabling testers to react to potential problems faster.

AI-based tools for both manual and automation testers

AI tools can support both manual and automation testers as they offer solutions that are universal and enhance various stages of testing.

LLMs

Language models such as OpenAI GPT, Google Bard and Claude can be used to generate test scenarios and documentation as well as automatically analyze error reports and generate responses based on system logs. In addition, they’re useful for creating test data. They can also suggest solutions strictly related to the code – from the most basic questions from an aspiring automation specialist to code reviews or algorithm optimization.

AI-powered visual testing tools

These tools enable you to automatically compare screenshots or user interface (UI) elements to detect visual changes in an app. AI ​​analyzes the differences and can classify them as a regression or a potential bug. Examples include Applitools, Percy and visual AI testing software. These solutions are particularly useful in testing applications where the UI plays a key role.

AI-based tools for automation testers

Test automation is an area where AI tools can significantly streamline the process of creating, executing and analyzing tests. Here are some examples of tools to consider.

Code generators

Language model-assisted tools, like GitHub Copilot and Codeium, can generate test code based on a natural language description, such as a method name or comment. Additionally, by analyzing the entire solution, they can suggest the next steps of a test. They’re especially useful for automation testers because they enable them to quickly create test scripts in multiple programming languages, which saves time on writing code manually.

Code quality monitoring and automated security analysis tools

AI supports static code analysis and security by detecting potential vulnerabilities and errors in the code before it goes into production. Examples of these tools include SonarQube, Checkmarx and Veracode, which, due to AI integrations, streamline code analysis by automatically suggesting fixes and solutions. Adding such a tool to a CI/CD process helps you find potential problems even before the code review stage.

Benefits of using AI in software testing

AI offers several advantages in the work of a software tester, enhancing efficiency, accuracy and coverage. Here are the key benefits:

• Improved Test Automation: AI can assist in writing and generating more complex methods, as well as identifying potential bugs in the code.
• Enhanced Test Coverage: AI tools can analyze vast amounts of code and generate comprehensive test cases, ensuring broader coverage and identifying edge cases that might be missed by manual methods.
• Smarter Test Case Generation: AI can generate test cases based on user behavior patterns, improving relevance and aligning testing with real-world usage.
• Cost and Time Savings: By reducing manual effort and improving accuracy, AI helps organizations save time and resources in the testing process.
• Enhanced Decision-Making: AI provides insights and recommendations based on data, enabling testers to make more informed decisions.
• Generating Large Amounts of Data: AI can easily assist testers in generating large volumes of data for testing purposes, streamlining the process and improving test reliability.

Incorporating AI into software testing enables testers to work more effectively, ensuring higher-quality outcomes with reduced effort. These tools not only help speed up processes, but also improve the quality of testing, reduce the number of errors and facilitate testing tasks at various levels of software development. Regardless of whether you are a manual or automation tester, or perhaps you operate on the border of both worlds, AI tools can significantly boost the efficiency of your work. It’s worth starting to experiment with these modern technologies now and benefit from their enormous possibilities.

How does AI impact software testers?

The development of AI opens new possibilities for everyone, including manual and automation testers. While the benefits gained by test automation engineers might seem more straightforward, AI can also streamline and enhance the work of manual testers.

By leveraging the AI tools mentioned earlier, software testers can improve the process of creating test cases, scenarios, steps, test plans and other documentation as AI can produce various types of documents according to a given example or criteria. AI can also help with risk management and test coverage by finding discrepancies between provided documentation and test scenarios.

These tools can also be used to create test data according to specification and predict future data by analyzing past information. You can also apply AI to build automated tests based on documentation and information about the tested elements.

While AI can significantly speed up some testing processes, it also poses some challenges. First, these tools are not bugless. For example, any AI-generated documents need to be reviewed for potential mistakes. The severity of mistakes can differ – from low, like using incorrect names, to critical, like providing a description of a module that has never existed. It’s important to always supervise AI tools and their contributions to ensure accuracy and alignment with requirements.

Additionally, when using AI, software testers should pay particular attention to data privacy and protection, especially when it comes to confidential data. Third-party tools might want to use the data you input to further train their models. That’s why it’s crucial to understand your company’s and your project’s guidelines when you’re considering enhancing your software testing processes with AI tools.

Still, AI solutions offer meaningful advantages to software testers, which can’t be ignored. Given the impact of this technology on software development and beyond, it’s crucial for both manual and automation testers to expand their AI skills and knowledge to be able to leverage these tools in their projects, when applicable. A test engineer that understands the power of AI and can effectively apply it becomes a one-man testing army.

Increase effectiveness with AI tools

AI offers tools with enormous potential that can revolutionize the way teams work, communicate and test software. AI speeds up processes, improves work quality, reduces errors and simplifies tasks both in manual and automation testing. However, it still requires human supervision to avoid errors and fully utilize its capabilities.

If you’re interested in accelerating your digital transformation and leveraging AI technologies with the help of experienced software experts, get in touch with us via this contact form.