GenAI-driven testing is a game-changer for software QA. It enables faster timelines, better use of scarce and specialized testing engineers, and much greater coverage and bug discovery. Accordingly, planning a GenAI-driven testing project is very different from planning a traditional testing project. This blog post explores six transformative aspects of AI-driven testing with an eye towards how they reshape the planning landscape.
Note that this post, like all posts in the Appvance blog, uses the Appvance Intelligent Quality (AIQ) GenAI-driven testing platform as its reference.
1. Human-Automation Synergy
The paradigm shift begins by recognizing that humans and AI can collaborate synergistically. Contrary to the traditional approach of manually scripting every test case, the future lies in strategically human-automating roughly 10-20% of test cases. These carefully selected cases should focus on business-critical functions where financial, reputational, and even criminal risks are at stake. This judicious allocation allows human testers to focus on high-impact areas while AI handles the bulk of the testing workload.
Please refer to my recently posted Cheat Sheet: Getting Ready for AI-driven Test Projects for more color.
2. Astonishingly Fast GenAI-driven Test Creation
One of the most compelling advantages of GenAI-driven testing is its amazing acceleration of test script creation. GenAI-driven test creation not only outpaces traditional methods but does so at an astonishing rate: 10X faster than with popular tools like Selenium. This reduces the time and effort required for test case development, and enhances the agility of the testing process.
3. Training AI vs. Human Scripting
Addressing the common concern of AI learning curves, the time investment required to train AI is comparable to the time required for humans to manually script the designated 10-20% of test cases. The learning phase for AI is an upfront investment that pays dividends in the short, medium and long run as it continually refines its testing capabilities based on evolving requirements.
4. Exponential Application Coverage
AI’s ability to comprehend complex scenarios and automate tests across diverse application functionalities leads to a remarkable 10x increase in coverage compared to manual test creation. This ensures a more thorough examination of the application-under-test (AUT), and also enhances the project’s resilience against unforeseen challenges.
5. Resource and Time Efficiency
A corollary of the increased efficiency is the significant reduction in both resources and time required for GenAI-driven testing projects. Compared to traditional approaches, AI-driven testing projects consume only 25% of the resources and time, making them a compelling choice for organizations seeking to optimize their testing processes.
6. Unveiling Bugs at Scale
The efficiency gains achieved through AI-driven testing projects are not without substance. By automating the majority of test cases, a GenAI-driven platform like AIQ typically discovers 10X the number of bugs versus manual testing. This heightened bug detection capability is a testament to the effectiveness of AI in identifying vulnerabilities that might escape human scrutiny.
Please refer to my recent post titled 7 Impacts of AI-Driven Testing on Dev Teams for more color.
Conclusion
The future of software testing is intertwined with artificial intelligence in general and generative AI in particular. By strategically allocating tasks between human testers and GenAI, organizations can enhance their testing efficiency while also fortifying their applications against potential risks.
