Top 5 Open Questions on Generative AI 

Introduction

The invention of Gen AI applications like ChatGPT marked a significant turning point in the Artificial Intelligence (AI) field, sparking a wave of creativity rarely seen before. Today, we are integrating Gen AI with everything—unlocking new performance frontiers. 

Yet, as Generative AI moves beyond its initial wave of excitement, the real challenge lies in leveraging its full potential effectively and responsibly. 

As per Gartner – “Generative AI isn’t just a technology or a business case — it is a key part of a society in which people and machines work together.” 

In a world where digital ecosystems evolve at lightning speed, traditional software testing methods often struggle to keep pace. You can leverage Generative AI  Intelligent Testing’s potential to transform your automation efforts and redefine how applications are tested, optimized, and secured.

With that in mind, let’s explore some of the biggest open questions surrounding Generative AI in software testing and what it means for the future.

Beyond the Hype: The Real Impact of Generative AI?

Gen AI can create content, code, test…Gen AI can create personalized experiences…Gen AI can enhance application quality…

According to Gartner, Gen AI is becoming a general-purpose technology, and the impact of generative AI will grow as people and enterprises discover more innovative applications for the technology in daily work and life. By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.

For example, Generative AI can automatically generate diverse test cases based on application functionalities, and such AI-Generated Test automation is 10X faster! By generating new synthetic datasets, Generative AI assists in overcoming limitations imposed by inadequate or unbalanced information. It can also enhance security measures as its inconceivable behavior provides severe obstacles for cybercriminals aiming to crack coded patterns and systems.

Question #1: Can Gen AI learn and evolve?

Just as human testers learn and refine their skills over time, AI models improve with continuous learning. As the AI model is exposed to more scenarios, it can accurately create better test cases and test data that reflect the system’s behavior. AI models can learn and improve, saving valuable time and resources; allowing QA teams to focus more on complex tasks that require human intuition and creativity. You can also update your datasets and retrain the model whenever there are any changes to the application. Generative AI has the ability to self-update test cases based on code changes, reducing maintenance overhead. By leveraging self-healing test automation, AI can detect UI and API modifications and adjust scripts accordingly.

Question #2: How will Generative AI contribute to business value?

Generative AI isn’t just about improving software quality—it’s also about delivering business impact. Generative AI provides new and disruptive opportunities to increase revenue, reduce costs, improve productivity, and better manage risk. 

These are some of the business benefits that can be achieved by using AI in testing.

Question #3: Can you track and measure its ROI

Gartner recommends connecting use cases to KPIs to ensure that any project either improves operational efficiency or creates net new revenue or better experiences. Some of the quality assurance metrics you can track are:

• Test Case Generation: Track improvements in the speed of Ai test case generation compared to manual testing methods.
• Test case Coverage: Gen AI can generate positive and negative test cases and edge-case scenarios, with no manual intervention involved, increasing overall test coverage and reliability.
• Test Execution Time: Track the time reduction for fixing a bug by automating test cases with Gen AI.
• Defect Detection Rate: AI-powered testing can uncover hidden defects earlier in the development cycle, reducing costly post-release issues.
• Reduction in Test Maintenance: Self-healing AI minimizes the effort required to maintain automation scripts, lowering operational costs.

When calculating the overall ROI of Gen AI in testing, meeting certain guidelines is crucial to ensure ethical, secure, and transparent use of this technology:

• Ensure that they are developed ethically, securely, and responsibly. 
• As Gen AI can evolve based on new inputs, maintaining and refining their accuracy is an ongoing process. 
• Selecting the right partner is critical as you embark on a generative AI journey.

Question #4: What are the risks of generative AI?

Generative AI creates artifacts that can be inaccurate or biased, making human validation essential.

• Lack of Explainability: Gen AI often lacks transparency, and it’s difficult to always understand everything about how they work. For example, why did the AI generate a particular test case?
• Absence of Contextual Understanding: While GPT can produce coherent replies, it often lacks a deeper understanding of the context. This results in inaccurate responses.
• Ethical Concerns: There’s the risk of producing harmful, inappropriate, or biased content as they base outputs on input data that may contain such biases. 
• Privacy and Compliance Risks: Concerns around privacy, regulatory compliance, and potential data leaks make it essential to implement robust security measures to safeguard sensitive information.
• Dependence on Data Quality: The performance of these models is reliant on the quality and quantity of training data. Garbage in, garbage out applies here too. 

Question #5: Can you combine Gen AI features with other Intelligent AI solutions?

While Generative AI brings creativity, adaptability, and efficiency in test generation, Intelligent Automation ensures precision, execution, and optimization – creating a powerful approach to quality assurance.

Approaches like Shift left Testing with Gen AI test case generation lead to earlier defect identification and fixing. Lower post-production defect rates mean fewer resources spent on fixes.

The Bottomline

One thing is clear: Generative AI isn’t just changing technology’s

changing the way we experience the world. And the future of QA isn’t just automation’s AI-powered intelligence that drives efficiency, accuracy, and innovation. 

The Webomates Testing-as-a-service utilizes the power of Generative AI to create unique and adaptive test strategies and test cases. AI-driven tools like Defect Predictor enable faster, more precise testing. The patented AI testing platform also helps reduce infrastructure costs. 

Click on this link to start a free trial. Or drop a message at info@webomates.com, and our team will reach out to you to steer your testing regime towards new horizons!

Tags: Ai test automationn, AI Testing, Automation Testing, Genrative Ai, Software Testing, Test Automation