GenAI in software testing emerged as a crucial solution, helping organizations align testing speed and efficiency with the pace of software development. If we look at software testing processes over the years, manual testing has played a crucial role but has also faced challenges in terms of speed and accuracy, especially with repetitive, complex, and time-consuming tasks. This led to the adoption of automated testing, which helped but still needed manual test creation and management.

GenAI fills the gap by reducing the need for human intervention. It can generate data in large amounts based on patterns learned from existing data sets and improves processes like predictive analytics, smart test execution, defect analysis, and test maintenance. Its main strength is in streamlining repetitive tasks and boosting efficiency, adding valuable capabilities to software testing.

Table Of Contents

1 What is Generative AI in Software Testing?
2 How is GenAI-Based Testing Different from Traditional Test Automation?
3 Benefits of Generative AI in QA
4 Challenges of Generative AI in QA
5 Developing a QA Strategy with Generative AI
6 Generative AI Testing Tools
7 The Future of Generative AI in Testing
8 Conclusion
9 Frequently Asked Questions
- 9.1 What is the use of GenAI in SDLC?
- 9.2 What is the best generative AI-testing tool?

What is Generative AI in Software Testing?

According to Market.us, “the Global Generative AI in Testing Market size is expected to be worth around USD 351.4 million by 2034, up from USD 48.9 million in 2024, growing at a CAGR of 21.80% from 2025 to 2034.”

But what exactly is GenAI? Generative AI (GenAI) is a type of artificial intelligence that can generate new content like text, images, videos, music, and more. What makes it even more powerful is its ability to learn from existing data and adapt to new challenges. The recent buzz around GenAI is largely due to user-friendly tools like ChatGPT, DeepSeek, Gemini, Microsoft Copilot, and DALL-E.

In the GenAI testing process, the first step is data collection, where the AI learns from existing test data and identifies patterns. It then uses this knowledge to generate relevant test cases. Next, it executes these tests and analyzes the results to detect defects. If issues are found, GenAI can help automate defect analysis by identifying the root causes. It also assists in test maintenance by automatically adjusting test scripts for certain changes in the software, reducing manual effort.

Generative AI for software development has proven its ability to automate repetitive tasks with high accuracy, often outperforming humans. This efficiency naturally extends to software testing. Software testing costs companies around 15-20% of their total project budget, and as per Statista around 23% of their annual IT budget is dedicated to testing purposes. Various studies suggest that GenAI can boost productivity by 30-40%, leading to significant cost savings for organizations.

How is GenAI-Based Testing Different from Traditional Test Automation?

Generative AI in software testing comes with several new features compared to traditional automation. Let’s see some of the important features:

No dependency on scripts– GenAI learns from existing data and user interactions and uses deep learning algorithms to create test scenarios by understanding the functionalities of the application and user behavior.
Adaptability– The AI tool is trained with advanced machine learning (ML) models that recognize patterns and processes, allowing it to adapt to different testing needs.
Natural language support– It uses NLP (Natural Language Processing), allowing testers to write test cases in plain English. It then converts them into test scripts. With this low-code ability, teams can focus on more critical tasks.
Predictive defect analysis– GenAI identifies patterns in test results that suggest defects and helps pinpoint the root causes. It also evaluates how these defects could affect the overall functionality and user experience of the application.
Optimized test coverage– Humans can overlook certain edge cases while GenAI in testing can boost test coverage by studying workflows, logs, and user interactions to ensure that all functionalities are tested.
Simulation of complex user behavior– By learning from user data, it can imitate real-world user interactions.
Seamless Integration– Many AI-driven test automation tools like Testsigma GenAI Copilot, integrate effortlessly with CI/CD pipelines, making them ideal for Agile and DevOps workflows.

We have discussed the most important features of generative AI for software testing but even more capabilities are on the horizon.

Benefits of Generative AI in QA

Let’s look at how generative AI in software testing helps testers in their daily workflow and organizations in optimizing their testing processes.

Automates Script Writing– After analyzing the functionalities of an application, it automatically generates test scripts, regardless of the programming language. This significantly reduces manual efforts, eliminates human errors, and most importantly saves time.
Optimizes Test Coverage– It can run detailed tests across different browsers, devices, and platforms. GenAI learns from existing large datasets and uses algorithms to detect test scenarios and usability issues. This saves testers from repetitive tasks and prevents unexpected errors.
Speeds Up Testing– By effectively automating test script generation and optimizing test maintenance, GenAI accelerates the entire testing process, leading to faster development cycles and reduced time-to-market for software development.
Reduces Testing Costs– There are significant cost savings achieved by reducing testing time, improving overall accuracy, and minimizing maintenance efforts. This in turn leads to improved product stability which means fewer unexpected downtimes, boosting the overall return on investments.

Challenges of Generative AI in QA

While GenAI in software testing has many advantages, certain challenges also need to be addressed:

Irrelevant Tests– GenAI is still on the development path, and its ability to understand complex concepts is limited as of now. As a result, some unnecessary test cases can be generated.
Computational Training Requirements– AI models like Generative Adversarial Networks (GANs) and transformers need a lot of computing power to train and run. While larger teams focused on improving their testing processes may not see this as a big problem, smaller teams with fewer resources might find the cost and technical demands challenging.
Need for Quality Training Data– The quality of the training data determines the accuracy of results generated by GenAI tools. Diverse and high-quality data is required and poor-quality or incomplete data can lead to inaccurate test results.
Difficulty in Interpreting AI-Generated Tests– Sometimes certain tools and skills might be required to understand and interpret AI-generated test results, especially when tests fail.

Developing a QA Strategy with Generative AI

Step 1: Set Clear Objectives

– Define what you want to achieve with GenAI in testing. Are you focusing on performance, security, usability, or scalability?

– Identify key challenges like test coverage gaps or high maintenance efforts.

– Having a clear objective will help you maximize the benefits of GenAI.

Step 2: Select a Suitable Tool

-Choose a GenAI tool based on your testing needs (functional testing, performance testing, visual testing, and so on) and the function you want the tool to perform, such as test case generation.

– Ensure the tool integrates easily with DevOps, CI/CD pipelines, and other testing tools.

– The right tool will help automate processes and enhance efficiency.

For example, when you choose Gen AI-powered Testsigma – you can use the Testsigma copilot to generate test cases from user stories, screenshots as well as requirements.

Step 3: Enable the Team

– Ensure your QA team knows how to utilize and work with the tool.

– Provide the necessary training and resources to help them use it effectively.

– A well-trained team ensures smooth adoption and better results.

Step 4: Implement and Monitor

– Start using GenAI in key areas like test case generation and test script updates.

– Continuously monitor how well it performs and whether it meets your objectives.

– Keep optimizing the process based on real-time results.

By following these steps, you can successfully integrate GenAI into your testing workflow and improve efficiency.

Generative AI Testing Tools

Testsigma

Testsigma is a cloud-based, low-code test automation platform powered by GenAI. It lets you automate web, mobile, desktop, and API testing all in one place. By simplifying test creation, execution, reporting, and maintenance, it makes test automation 10x faster.

Designed for both technical and non-technical users, Testsigma supports codeless and code-based testing on the same platform, speeding up the testing process while ensuring high-quality results.

Key Features:

Zero Setup Time – Create a free trial account and start testing on the cloud, since there is no need to set up infrastructure.
Easy to Learn– Write automated tests in plain English using NLP-based testing.
Data-Driven Testing– Automate real-life test cases with data-driven capabilities for more reliable results.
Seamless Integrations– Easily connects with CI/CD tools, project management, collaboration, and bug-tracking tools.
Detailed Test Reports– Provides actionable insights, helping developers and testers collaborate effectively.

Testsigma Copilot Provides Gen AI-Powered Assistance for QA Teams

Testsigma Copilot is a GenAI-powered assistant designed to enhance testing quality and efficiency. It helps QA teams by providing smart test suggestions throughout the testing process.

Auto-generates test cases from user stories, prompts, or Figma designs.
Suggests additional user scenarios to improve test coverage.
Achieves 70% automation coverage, even for edge cases.
Generates API test cases automatically for seamless API testing.

TestCraft

Previously known as TestCraft, Perfecto Scriptless is a web and mobile testing tool designed for both manual and automated testing. It enables fast test execution for web-based applications while maintaining high industry standards. The tool is also scalable and ideal for running repetitive tests efficiently.

Key Features:

Multi-Language Support– Write test scripts in different programming languages.
Copy-to-Clipboard– Easily copy and paste test scripts for quick reuse.
AI-Powered Test Generation– Uses advanced GPT technology to generate test ideas for websites.
Seamless Integrations – Connects with other tools for a streamlined testing workflow.

Appvance

Appvance is a generative AI-powered testing tool designed to automate testing with minimal manual effort. It aims to maximize test coverage and optimize test maintenance.

Key Features:

AI-Powered Test Generation– Analyzes test runs and automatically creates test cases for missing areas, enabling exploratory testing.
Enhanced Application Visibility – AI explores multiple paths within the application, exposing hidden test cases and improving coverage.
Early Defect Detection– Generates a health chart of the application, helping teams identify potential issues early.
Low-Code & Codeless Test Creation– Supports low-code test scripting or allows testers to interact with the application manually while converting actions into test cases.
Multi-Platform Testing– Supports testing across web, mobile, APIs, and other services.

Katalon

Katalon is a widely used test automation tool that creates, runs and manages automated tests. It supports web, API, mobile, and desktop testing with powerful AI-driven features like self-healing, automated test generation, AI analytics, and NLP-based test scripting.

Key Features:

Smart Wait Technology– AI dynamically adjusts wait times for web elements to improve test stability.
Seamless CI/CD Integration– Works smoothly with DevOps pipelines for better reporting and analytics.
Self-Healing Test Scripts– AI detects and fixes broken test scripts automatically.
Advanced API Testing– Supports testing SOAP, REST, and GraphQL APIs.
Flexible Test Creation– Offers scriptless test building for beginners and advanced scripting for complex scenarios.

Tricentis TOSCA

Tricentis TOSCA is a widely used test automation tool that offers end-to-end testing through a model-based approach. It allows testers to create reusable test assets with minimal scripting, making automation faster and more efficient. TOSCA leverages AI for test case design, self-healing scripts, and intelligent test data generation.

Key Features:

Risk-Based Testing– AI analyzes past test data to predict failure-prone areas and prioritize test cases.
Smart Test Data Management – Uses AI to generate realistic test data for better accuracy.
Broad Technology Support– Works with web, mobile, API, desktop, and enterprise applications like SAP.
CI/CD Integration– Supports DevOps workflows for seamless continuous testing.
Powerful Reporting & Analytics– Provides detailed insights for better test management.
Model-Based Automation– Enables script-less test creation and reusable test assets.
System Simulation– Can test applications in isolation by simulating dependent systems.

This is just a brief overview. Many AI testing tools are available, each with unique features that make test automation more efficient and accessible.

The Future of Generative AI in Testing

Research on GenAI in software testing is rapidly advancing, with significant growth expected in the coming years. According to a July 2024 report by Capgemini, “Generative AI is set to be adopted by 85% of the software workforce over the next two years,” indicating a significant rise in its usage for software testing too.

Here are some key developments anticipated:

Test Prioritization – AI will assess risk and impact to prioritize tests, ensuring critical areas are tested first.

Self-Learning Systems – AI will continuously learn from test results, improving test case generation and adaptability.

Seamless DevOps Integration – GenAI will integrate more smoothly with CI/CD pipelines, making automated testing more efficient.

Advanced Predictive Analytics – While self-healing capabilities exist, future AI will predict failures and enable proactive testing.

Defect Analysis– AI will analyze test results to detect and classify errors, streamlining the debugging process.

Conclusion

Now you know what GenAI is, the relevance of generative AI in software testing, its benefits, and its challenges. We also covered how to develop a QA strategy with GenAI and explored popular AI-driven test automation tools. It’s clear why organizations are adopting GenAI to optimize operations and drive growth in software testing and development.

It’s also important to note that having the right tool makes all the difference. For teams looking to stay ahead with an intelligent testing platform, Testsigma is a strong choice. It’s highly user-centric and offers exceptional ease of use, allowing you to generate test cases in plain English in as little as two minutes. This is the kind of solution that brings the right dynamics to your testing team.

Frequently Asked Questions

What is the use of GenAI in SDLC?

GenAI is used in automating various activities in the Software Development Life Cycle (SDLC) such as requirement gathering, design, development, software testing, and deployment. It not only automates but also enhances the efficiency of these tasks. Ultimately, the implementation of GenAI accelerates the speed of the entire SDLC.

What is the best generative AI-testing tool?

There are many AI-driven test automation tools available, each offering different use cases. However, analyzing the recent trends of many enterprise users, Testsigma seems to be an ideal option for teams trying to automate and streamline their testing processes. This no-code platform can be used by testers with even zero programming experience.

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