Testing for tomorrow: Incorporating AI in quality assurance testing

The current state of quality assurance

Quality assurance is a systematic and comprehensive approach to ensure that software products meet desired quality standards.

Today, the QA community continues to rely on manual testing, which is time consuming and prone to human error. Meanwhile, automation testing is useful, but high costs are a barrier for most businesses. Quality assurance can represent up to 40% of a project’s budget.

Integrating AI into quality assurance can lower costs and save time, allowing testers to deliver faster and higher quality work than ever before.

The rise of artificial intelligence in quality assurance

Almost two years after the launch of ChatGPT, the landscape of quality assurance is being transformed by artificial intelligence.

Many teams have started using AI in their regular testing, and we’re seeing industry examples of AI being used for design test validation, leading to reduced human input.

With the right data, artificial intelligence tools can learn code and start generating automation tests, lowering the need for manual testing. Additionally, AI can recognize patterns in test data and develop test scripts faster and more efficiently.

One example is how test cases are now created through behavior driven development (BDD) frameworks with Gherkin, a business-readable, domain-specific language used to describe software behaviors without detailing how they work.  

Here are several testing strategies AI can supercharge:

• Manual testing. Traditional manual testing is time-consuming and prone to human error. AI enhances testing by speeding up test case creation and execution and reducing mistakes. AI-generated scenarios support testers’ focus on exploratory testing and meet the growing demand for continuous testing.

• Test automation. AI can revolutionize automation testing by generating intricate, effective scenarios through the analysis of massive data sets. This optimizes coverage and identifies trends, which speeds up testing without compromising quality.

• Regression testing. Regression testing ensures changes don't interfere with existing capabilities. AI makes this more efficient by choosing applicable test cases more quickly, saving time and money.

• Exploratory testing. AI technologies enhance exploratory testing by using software to intelligently navigate, identify potential issues and test specific areas. AI's capacity to replicate human-like interactions effectively reveals hidden flaws and shortcomings.  

AI challenges and skepticism

AI brings challenges as well as benefits to the testing landscape.

AI efficiency is directly related to the size and quality of the data it uses, with lower quality data leading to more inaccurate outputs. This is especially concerning in complex areas such as automation testing.

Efficient AI needs intricate algorithms and vast amounts of data to handle various logical and unpredictable scenarios. Additionally, AI's decision-making process is often hidden, making it hard to fully trust its conclusions.

These challenges require a patient and thoughtful approach to AI integration in quality assurance.

The future of AI in quality assurance

As AI becomes embedded in quality assurance processes, software development is changing. AI is bringing revolutionary advancements, reducing costs and time associated with manual testing, and enhancing overall accuracy and efficiency.

AI-driven techniques are already changing how work is done, from expediting the production of test cases to optimizing regression and exploratory testing.

AI offers clear benefits. It speeds up test creation and execution, reduces human error, and frees up QA teams to work on more complex testing. By intelligently selecting relevant test cases, AI makes regression testing more efficient, ensuring new changes don't disrupt existing functionality. Additionally, AI mimics human interactions, uncovering easy-to-miss flaws that conventional techniques can miss.

However, there are some obstacles that AI faces when it comes to fully integrating across QA. The volume and quality of data that artificial intelligence processes have a significant impact on its efficiency. Poor data quality can lead to inaccurate results, especially in complicated domains like automation testing where large amounts of data and complex algorithms are required.

What does the future look like for quality assurance?

The future of quality assurance is already here. New advancements in AI and machine learning (ML) mean testing is becoming faster, more reliable and more efficient.

Automation tests will be quicker. Testing time will decrease. Test generation will be more intelligent, and defect analysis will be optimized.

These changes will give quality assurance teams more time to focus on complex and time-consuming tasks while delegating repetitive tasks to AI.

AI is a powerful assistant, but it cannot entirely replace the creativity, intuition and critical thinking of human testers. The future is bright, but we should continue to tread with caution.

To learn more, have a look at how Valtech is using data and AI to create process efficiencies across a variety of industries and use cases.