How to Use Generative AI in Software Testing

What’s the one thing keeping your software from being perfect? It’s not your process, your tools, or even your team. It’s time.

Time to debug every issue.

Time to test every edge case.

Time to ensure your code doesn’t break when it matters most.

Of course, there’s no way to buy more time. No matter what you do, there will always be 24 hours a day. However, what can be done is leverage Generative AI. It makes you work faster.

In this blog post, we’ll explore the role of generative AI in testing in detail, focusing on its applications in software testing.

What Is Generative AI?

Simply put, Generative AI refers to sophisticated Machine Learning (ML) models (also known as deep learning models) that can generate high-quality text, images, audio, and other forms of media based on the data they are trained on.

These models simulate the learning and decision-making processes of the human brain. They identify and encode patterns and relationships within large volumes of data to understand natural language requests and respond with relevant content.

Generative AI has introduced tools and techniques in software engineering that enhance productivity, accuracy, and innovation.

From code generation and refactoring to automated version control and performance profiling, the technology is reshaping how software is designed, built, and maintained.

It can literally take your idea and requirements for software and convert them into user stories — basic explanations of features relevant from the end user’s perspective. This not only accelerates development but also improves the overall quality of the final product.

Benefits of Generative AI in Software Testing

What about testing, you ask?

The advanced AI technology covers that, too.

Unlike traditional approaches, which involve time-consuming manual tasks, generative AI testing helps you focus on strategic oversight.

For example, it improves the quality of non-functional testing, including load, performance, and security testing. Generative AI in software testing can simulate thousands of users interacting with an app to detect real-time vulnerabilities.

This level of scalability is hard to achieve manually.

Secondly, it makes predictive testing possible by analyzing past defect patterns and historical testing data. It can tell you where bugs are most likely to occur in the software.

Lastly, Generative AI automates scheduling and resource management and provides accurate timelines for testing. This allows you to allocate the correct number of hours and human power to test different use cases of the software in question.

Core AI Models Behind Modern Generative AI Testing Tools

There are typically three types of Generative AI models in the context of software testing:

1. Large Language Models (LLMs)

LLMs are computational models that use ML to perform Natural Language Processing (NLP) tasks. Tools like ChatGPT, Bard, and Claude can be used to create test cases, write scripts, and draft documentation. They can understand natural language and translate it into actionable testing assets.

2. Transformer-based models

These types of neural network architecture apply a transformer model to perform various tasks, such as language translation, sentiment analysis, and machine translation. Models like BERT or specialized versions (e.g., TestBERT and CodeBERT) are used for bug detection and code analysis.

3. Diffusion models for test data synthesis

These are a type of Generative AI models that utilize a “diffusion process” to create realistic synthetic test data by gradually introducing noise to real data during training.

Diffusion models reverse the noise addition process to generate synthetic data resembling the original data distribution. However, their use in testing sensitive systems still requires careful validation to ensure compliance with privacy standards.

How to Use Generative AI for Software Testing

1. Create adaptive testing frameworks

In fast-paced DevOps environments, the buck doesn’t stop at quality assurance. It’s also important to keep up with rapid releases. Generative AI in software testing fits perfectly into CI/CD pipelines and offers assistance at every stage.

You know the drill: every new feature or update requires dozens, sometimes hundreds, of test cases. Writing them manually is time-consuming and prone to human error. This is especially true for SaaS and mobile app testing, where changes are frequent.

Fret not — Tap into Generative AI’s ability to create diverse test cases.

Have a subscription app whose functionality has changed? No problem.

You can generate new test cases for scenarios like expired credit cards, simultaneous logins on multiple devices, or network interruptions during payment. This automation minimizes the risk of overlooked edge cases and, in fact, broadens your test coverage.

In addition, it can propose different scenarios you haven’t even considered for testing software, such as switching between mobile and desktop mid-session or accessing certain features in unexpected orders.

With generative AI-based software testing, the creative possibilities are truly endless. For example, it might detect that a login form is not fully accessible, flagging missing ARIA labels for screen readers. It could be recommended that these labels be added to ensure compliance with accessibility standards (e.g., WCAG). This optimizes your load testing efforts.

2. Automate your scriptwriting

Building Cypress, Selenium, or Appium boilerplate code takes time and energy. Why not delegate the grunt work to Generative AI? Simply describe the test you want to run, and it will generate the initial script.

For example, you could say, “Write a Selenium script to test a user logging in with both valid and invalid credentials.”

It will not be the perfect draft, but it will be a strong start. You can then take it forward and refine it however you like. Working with Generative AI on test scripting writing won’t replace your expertise; it will only amplify it.

3. Generate complex test data

You still need to build datasets for edge cases or realistic simulations in software testing. For example, if you’re testing a banking app, you need account numbers, transaction IDs, and even customer profiles to check every user story and system log.

Now, you can ask your team to create a ton of data for you from scratch. Or, you can get Generative AI to produce realistic and diverse yet synthetic test data capable of mimicking real-world scenarios.

In another example, when testing a healthcare software solution, you would require thousands of patient records with varying conditions, demographics, and treatments. Generative AI in software testing can take off your hands and give you data that adheres to privacy regulations like HIPAA and GDPR.

4. Test your APIs with precision

APIs are a core component of software systems today and are increasingly used in complex, distributed environments.

Your software will, too, invariably include APIs. These can be deployed anywhere, whether behind a DMZ or co-located with cloud-native apps and microservices. Therefore, it’s essential to protect their access at runtime, regardless of the deployment.

Generative AI ensures the API’s integrity, confidentiality, availability, and the data it processes. It can assist in generating payloads, validating responses, and suggesting potential attack vectors to check for vulnerabilities.

All you need to do is describe the API endpoint, and it will produce requests and assertions. What’s more — it can help identify trends in the API quality by tracking progress over time.

5. Simplify regression testing with every sprint

As your test suite grows larger with every test, identifying redundant test cases or those that provide minimal coverage can get tedious. With Generative AI, you can prioritize tests based on recent code changes and only run those that are more likely to uncover issues.

For example, suppose a new feature touches only the app’s payment module. In that case, it will only recommend tests to test that and not focus on other functionalities like user profile management.

With a lean and focused regression suite, you can minimize the time and resources spent while maintaining high-quality results.

6. Resolve critical bugs on priority

When you deal with multiple reports at scale, bug triaging can overwhelm you.

Thankfully, with Generative AI testing, you can analyze logs and stack traces and even provide descriptions of cluster issues. You can ask it to group the bugs based on modules or severity levels. This way, you’ll clearly understand what needs to be prioritized and resolved first.

7. Keep security at the forefront

We are well into the middle of the 2020s, and we’ve seen how security vulnerabilities can derail even the best apps and systems. Generative AI can aid in identifying potential threats, such as SQL injection, insecure authentication methods, and cross-site scripting (XSS), based on predefined patterns.

For instance, ​​it could identify that unnecessary ports are left open or default configurations are still in use for certain services. It may recommend closing unused ports and replacing default settings with secure custom configurations.

8. Receive reports for quick decision-making

Software testing isn’t complete without communicating results effectively. You may already have many competent Business Intelligence tools and dashboards designed for different audiences and stakeholders.

Generative AI takes this step to the next level.

It can help you transform logs, metrics, and raw data into polished reports, centralizing data visualization in a way that highlights key trends, such as pass/fail rates or performance bottlenecks.

Want Generative AI in software testing to oversimplify the results for you? It can do that, too. It can assist in organizing results by specific software testing components, urgency levels, and the teams responsible for addressing issues.

Ethical Considerations in Generative AI for Software Testing

Leveraging this technology for the best results is an ongoing effort. It demands a strong and secure digital core with balanced investments in both technology and people. Generative AI’s responsible use is critical for maintaining trust and fairness in software testing. Let’s see why:

1. Bias in AI models

The AI models are only as good as the data they’re trained on. If they are inclined toward specific software types, programming languages, or feature sets, Generative AI may overlook certain bugs or mis-prioritize test cases.

To mitigate this bias, use diverse and representative training datasets and validate them rigorously to ensure accurate checks across various domains and use cases.

2. Privacy and data protection

In some testing scenarios, AI models may encounter sensitive user information and inadvertently use it to refine their models, creating risks of unintended data exposure. Applying such data for software testing without robust safeguards can lead to privacy violations.

You don’t want to breach compliance with regulations like GDPR, CCPA, or HIPAA — they have strict penalties. Test data should be anonymized to address this, and synthetic data should be used where possible.

3. Accountability and oversight

Look, no matter how impressive Generative AI’s results may be, it often operates in a black-box manner. That means it could be difficult to understand why certain bugs are flagged and others aren’t. This lack of transparency can complicate accountability.

If generative AI testing overlooks a critical bug, who’s to be blamed?

Therefore, it’s essential to treat the technology as a partner rather than a decision-maker. Perform regular audits and clearly document how AI decisions can be improved.

4. Over-reliance on automation

Generative AI in test automation is highly effective. You don’t need to write the code or test scripts. It does all the work for you. However, this might lead to a decline in the quality of fundamental programming.

Your nuanced judgment is still needed to catch issues with user-facing features or complex edge cases. The good news is that ongoing training and periodic reviews of manual coding techniques can help you stay sharp forever.

Source: This blog was originally published at testgrid.io