Work Smarter, Not Harder: Using AI to Supercharge Test Automation

AI is one of the most game-changing tech innovations today improving business operations across different industries. Bill Gates stated in his blog that the emergence of AI was as significant as the establishment of the Internet.

Businesses worldwide are increasingly embracing innovation through the power of AI. Siemens, a global technology powerhouse, leverages AI to boost operational efficiency by advancing coordination and time management, refining malfunction identification, and stabilizing production procedures.

Rolls-Royce, an engineering company specializing in automobiles and aerospace technologies, together with Singapore’s DSTA, developed aviation upkeep systems (digital twins) that use machine vision to reach hard-to-access engine parts and detect probable issues before they lead to costly repairs or disruptions.

KFC, in partnership with Baidu, introduced a regional facial recognition system that evaluates age, gender, and facial expressions and recommends menu items that align with the customer’s mood and tastes.

So, it should be no surprise that QA and software testing are no exception. According to the World Quality Report 2024-25 (WQR), 34% of respondents are already actively utilizing Gen AI in their quality engineering processes. Even with the complexities and considerable concerns surrounding AI implementation, its combination with test automation can potentially bring various advantages for businesses.

In this article, let’s see what prospective advantages companies can derive from AI-driven test automation, what hurdles they can confront, and the use cases of applying this technology.

Unlocking the Full Potential of AI-driven Test Automation

The WQR interviewees mentioned that they expect test automation to minimize various risks (54%), decrease the number of live issues (51%), and improve customer experience (50%). By strengthening it with AI, businesses can:

• Maximize velocity. AI-driven automated testing allows project teams to swiftly create tests and fix software issues, thus releasing and upgrading high-quality digital solutions faster meeting changing end-user preferences or market requirements.

• Expand coverage. AI enables QA specialists to write more scripts in less time and find unconventional scenarios that can be missed by humans. This helps cover more aspects and rectify issues before they escalate into full-fledged problems in the production environment.

• Effectively maintain tests. QA teams can spend less effort on updating tests. AI has the capability to analyze how the code of recently added software features can affect the code of current automated scripts and automatically provide smart alterations.

• Apply codeless opportunities. Nowadays, teams can choose from a variety of AI-driven codeless platforms that save time on manually writing scripts and ensure higher process efficiency.

• Lower expenditure. Swifter automated defect scanning and adoption of low/no-code solutions can allow companies to economize financial assets and redistribute available resources on other significant project activities.

• Improve reporting. AI processes vast amounts of data to generate insightful, visually appealing reports. They offer a clearer understanding of test failure causes and empower project stakeholders to make more informed decisions for enhancing software quality.

Analyzing Two Sides of a Coin: Hurdles to Overcome

Although AI can enhance the capabilities of automated testing, organizations should remember that its implementation isn’t always smooth sailing, meaning the following roadblocks can occur:

• Initial implementation expenditure. Before reaping the fruits, companies need to invest in QA professionals who are able to master new technology and learn quickly.
• Shortage of expertise. The WQR states that lack of skills (30% of respondents) remains a significant test automation challenge, which means that integration of AI and specific tools can pose even more complexities.
• Issues with data. To ensure that AI algorithms operate efficiently, companies should leverage only high-quality data that has no duplicates, discrepancies, or bias. Otherwise, test results may be inaccurate (e.g., false negatives).
• Integration problems. Without a detailed preliminary strategy, embedding AI into automated QA workflows may be tricky, which can constrain its potential and hinder probable benefits.

Top 5 AI Use Cases for Enhancing Traditional Test Automation

Let’s analyze some potential cases of harnessing AI to facilitate more efficient operations.

1. Improved regression testing

Regression testing ensures that newly introduced software features do not disrupt existing functionality. As the project evolves and the codebase grows, it becomes increasingly challenging for QA automation engineers to maintain comprehensive test coverage and ensure the reliability of the IT product.

Fortunately, AI has the capabilities to swiftly examine previous code alterations and testing outcomes to identify areas most prone to defects and suggest what verifications should be executed first. This helps overcome lackluster productivity and speed up software rollout.

2. More effective GUI verifications

First impressions do matter. Studies show that it takes only 50 ms for the target audience to decide whether they like your IT solution or not, while other research shows that 94% of reasons for rejecting a website were related to issues with design.

QA automation engineers can enhance their user interface testing capabilities with the help of AI. It allows them to automate the process of verifying UI elements with the help of image-based testing, which improves overall velocity and accuracy.

3. Next-generation scripts

AI can automatically and quickly generate intelligent scripts, ensuring a more effective quality control process. AI gathers data on how users work with the IT product and builds predictions on the expected outcomes. In case of any abnormal situations, AI highlights probable issues. Then, QA specialists analyze these messages to identify further actions - opening a defect or continuing testing.

4. Automated recovery opportunities

Due to AI’s self-healing possibilities, QA automation engineers can have less work with maintaining and updating scripts after code changes in the application. AI-driven automated scripts can adapt leveraged locators to alterations in the UI to ensure continuous test execution. This helps enhance the robustness of scripts, ease maintenance, prevent disruptions in the testing process, and dedicate more effort to performing other vital QA activities.

5. Better approach for working with issues

When AI supplements test automation, it can review previous application-related data and consider software intricacy. This helps to forecast areas where potential issues may arise, allowing QA specialists to focus more on risky software aspects. In addition, companies can leverage test automation frameworks with AI elements. They serve to better understand the reasons for diverse glitches, which makes it easier and faster for QA and development teams to mitigate them.

All Things Considered

By leveraging AI-empowered test automation, QA teams can potentially boost the productivity, precision, and velocity of testing workflows. Although AI’s introduction is associated with hurdles, this technology can facilitate regression and GUI testing, as well as provide effective self-learning, self-healing, predictive, and cause analysis opportunities.