AI in software quality assurance: How to get started

Software and applications are becoming more sophisticated by the day. As this effect escalates, quality assurance (QA) teams face phenomenal pressure to ensure software is free from defects, all while maintaining lightning development speed. While traditional QA approaches get the job done, they can’t keep up with the speed of iteration on software applications, especially in the new age of AI.

However, the same capabilities that AI offers for enhancing the speed of creating sophisticated software can be used to speed up the QA process. In this guide, we’ll learn how AI is reshaping the QA process. We’ll take a look at how AI can be incorporated into existing QA processes, the benefits it offers, and the challenges that come along with it.

What is software quality assurance?

Software quality assurance is the process of ensuring software is free from defects and meets a standard set of benchmarks in terms of performance and quality. This process involves planning tests, executing them, tracking issues and defects, relaying feedback to improve the process, and reporting the status of the tests to stakeholders.

Quality assurance helps you build a reliable and high-quality software product that can, in turn, drive your business goals

Why is quality assurance important?

Quality assurance helps you build a reliable and high-quality software product that can, in turn, drive your business goals. It provides a seamless experience to your customers while maintaining their trust in the product by ensuring that their functional and compliance requirements are met. Moreover, it reduces the cost of maintaining the software in the long run by eliminating expensive issues and failures early on.

“The cost of fixing a defect after release is significantly higher—up to 100 times—than fixing it during the early stages of development.” – IBM System Science Institute

Traditional methods of quality assurance

Traditionally, QA engineers have heavily relied on manually testing software to find bugs and usability issues. Over time, they started writing scripts or using automated testing tools to perform automated software testing. Automation of manual testing not only reduced the time for a testing cycle, but it also made testing in QA less prone to errors.

However, despite automated scripts and tools, QA engineers still have to maintain these scripts and update them whenever necessary. They also have to oversee the execution of these scripts to ensure that the whole testing process runs smoothly.

How has AI changed quality assurance?

Traditional automated testing methods struggle to incorporate the evolving complexity of applications. For instance, an e-commerce app today will become more sophisticated three months from now, with more powerful features and an updated interface. The original automation scripts in this case may not be adequate.

Luckily, AI can be used as a complement to traditional testing methods and can therefore significantly boost the productivity of QA teams and also the entire QA process. By leveraging AI-driven machine learning and natural language processing capabilities, QA teams can forecast issues and continuously update the testing scripts to meet the evolving complexity of an application.

Impact of AI on the QA process

When we integrate AI into QA, the following components of AI can positively impact the overall result of the QA process:

• Predictive Analysis: AI can analyze historical data related to an application’s defect patterns and use it to predict potential issues or areas of high risk. It can also give you a sense of usage patterns that lead to such defects or issues.
• Natural Language Processing (NLP): NLP can directly automate various test cases using data from user stories and other relevant documents.
• Machine Learning Models: Machine learning models continuously learn from a dataset and use their updated knowledge to improve a workflow. Using existing testing processes, these models can learn from the available test data and refine the testing process to prioritize critical tests.

Strategies for implementing AI in QA

Some strategies for implementing AI in QA are as follows:

• Automated Test Case Generation: Since AI algorithms can read application data and user interactions, they can also automatically generate and maintain test cases. This means that QA engineers no longer have the burden of writing and updating test cases whenever an application update has to go out, saving considerable time.
• Predicting Defects: Predictive analysis can be helpful, which uses historical data on defects an application has experienced and anticipates similar defects that can occur in the future based on certain changes. This can prompt QA engineers to communicate such potential defects to the developers, who can proactively ensure these defects are reduced in the application.
• Visual Testing: Visual testing is the process of visually testing an application and identifying UI issues across cross-functional devices and platforms to ensure a consistent and seamless user experience everywhere. AI-driven visual testing can be performed more rapidly and accurately, eliminating the need for manual visual testing, saving significant time and bandwidth for QA teams.
• Anomaly Detection: AI can detect anomalies or deviations from traditional behavior in real time, giving you a heads-up about these anomalies before your users start reporting them. By detecting these anomalies in real time, QA teams can reduce the time it takes to detect and resolve critical issues in production.

AI can significantly speed up multiple parts of the QA process, from running tests to writing them, thereby reducing the overall cycle time of a typical QA process

Benefits of using AI in QA

Here are some benefits of incorporating AI into QA:

• Enhanced Efficiency: AI can significantly speed up multiple parts of the QA process, from running tests to writing them, thereby reducing the overall cycle time of a typical QA process.
• Improved Accuracy: Human errors are mitigated with AI-driven tests, which increases the overall reliability and consistency of an application.
• Better Test Coverage: Large and complicated test scenarios may be more complex for humans to tackle, but AI can easily manage them and enhance the software test coverage to improve an application’s reliability.
• Cost Reduction: As AI starts taking over more manual and routine tasks, QA teams can become leaner, saving organizational resources.

Challenges of using AI in QA

Using AI in QA is not without its challenges. Here are some of them:

• Implementation Complexity: QA engineers who aren’t well-versed in AI may need to upskill themselves. This may require investing significant time and resources.
• Availability of High-Quality Data: Predictive analysis and self-learning machine learning algorithms require high-quality data. If the data’s quality is off, it can instead degrade the performance of the QA teams, as they will have to spend extra time cleaning this data.
• Maintaining AI Models: While modern AI QA platforms like Testim reduce maintenance overhead, in-house or custom AI models can be tedious and expensive to maintain and update. Technical expertise and substantial resources are required to maintain these models.

Despite the above challenges, the benefits of AI in quality assurance seem promising. AI models are becoming smarter and cheaper, which will reduce costs and lessen learning curves for QA engineers to integrate AI into the QA process.

Conclusion

Today, AI is not a hype-wagon, a trend, or a gimmick. It’s a completely different approach to software engineering as well as quality assurance. By providing benefits such as predictive insights, extensive automation capabilities, and unprecedented efficiency, AI can empower QA teams and supercharge their productivity, all while making applications more reliable.

However, QA teams must understand what AI brings to the table and how to make the best use of it. By leveraging AI extensively in their QA processes, teams can gain an edge in shipping quality applications and propelling their business outcomes.

This post was written by Siddhant Varma. Siddhant is a full stack JavaScript developer with expertise in frontend engineering. He’s worked with scaling multiple start-ups in India and has experience building products in the Ed-Tech and healthcare industries. Siddhant has a passion for teaching and a knack for writing. He’s also taught programming to many graduates, helping them become better future developers.