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WEBINAR
AI-powered testing is growing, but manual testing is taking a different trajectory. From writing and repeating tests to reviewing AI output and acting as an orchestrator, the tester’s role is shifting. How do you know what should stay manual, and where you can reliably use AI to help you today?
Join Nathan Jakubiak of Parasoft and guest speaker, Diego Lo Giudice of Forrester Research, for a fireside chat on market trends, including why ‘vibe coding’ requires ‘vibe testing’, and the evolving role of QA in an AI-driven software development lifecycle (SDLC).
Despite the advancements in AI, the data shows that manual testing is far from obsolete. In fact, a significant portion of testing is still performed manually. A recent survey indicated that between 20% and 23.6% of automation is achieved on average, with a large percentage of developers still performing manual testing either sometimes or always. This highlights a gap between the ideal of full automation and the current reality.
The role of testers is evolving. In the short term, testers will likely shift from writing automation scripts to reviewing and refining tests generated by AI. This is similar to how developers use code generators – the AI creates the code, but a human reviews and adjusts it. Testers will also need to understand the capabilities and limitations of AI tools, learning how to provide the right context for AI to generate better test cases. This involves understanding concepts like vector embeddings and Retrieval Augmented Generation (RAG).
As AI matures, testers might move into a more orchestrating role, managing different AI agents that handle various testing tasks. The core human element that AI can’t replace is the judgment of whether the current level of quality is sufficient for the business needs. This involves understanding the application’s look and feel, user experience, and overall intuitiveness – aspects that go beyond simple functional validation.
Integrating AI into testing workflows isn’t without its hurdles. One of the primary challenges is adoption – overcoming the fear of job displacement and encouraging teams to embrace AI tools. This requires significant upskilling. Testers need to learn about AI concepts, large language models (LLMs), and how to effectively prompt these systems.
The rapid pace of AI innovation also presents a challenge. New models, multi-modal capabilities, and agents are emerging constantly, making it difficult for organizations to keep up. Understanding what each new development means for testing and how to apply it effectively is an ongoing task.
Organizations need to assess the AI capabilities offered by their current tools and vendors. A key question is: what can this AI-powered tool do now that a standard tool couldn’t a year ago? This helps in understanding the real benefits and ROI of adopting AI. Without proper training and understanding of the tools, adoption can be slow and ineffective.
When thinking about the ideal tech stack for an AI-augmented software development lifecycle, it’s helpful to consider the testing pyramid. This model typically includes unit tests at the base, followed by integration (API) tests, and then functional and UI tests at the top. Static analysis is often included at the bottom as well.
AI can be applied across all these levels:
Beyond these specific applications, there’s a growing trend towards “vibe testing,” where testers work closely with AI agents in a more conversational and iterative manner. This requires a robust understanding of the underlying AI models and the ability to provide context, perhaps through model gardens that allow choosing specific LLMs or integrating with existing knowledge bases and vector databases.
To stay relevant, testers and QA professionals need to develop a new set of skills. Prompt engineering is paramount – learning how to effectively communicate with AI to get the desired results. This involves understanding how to ask questions, iterate on prompts, and treat interactions with AI like a conversation with an expert.
However, traditional testing skills remain important. Knowing what constitutes good quality, how to write effective test cases, and understanding business and technical coverage are still essential. These foundational skills inform the prompts testers create and help them evaluate the AI’s output.
Some testers will need to develop a deeper technical understanding of AI, including how LLMs work. Others might focus more on the “vibe coding” aspect, concentrating on business understanding and prompt creation. The key is to learn how AI tools work, understand their boundaries, and figure out how they can be used to improve efficiency, whether it’s generating test cases, triaging failures, or creating test data.
Ultimately, testers should aim to become the drivers of these AI tools, rather than being replaced by them. By understanding what AI can do and how to best utilize it, testers can enhance their careers and stay ahead of the technological curve.
For organizations looking to adopt AI in their testing processes, starting small is often the best approach. Develop a clear blueprint for how you want to leverage AI and begin with a small team. This allows for experimentation and learning without overwhelming the entire organization.
When considering ROI, don’t get bogged down in lengthy business cases. Sometimes, the benefit is clear enough. For instance, if an AI tool can save even a minute a day per developer, the cost savings can quickly justify the investment. The key is to get the tools into the hands of users and let them start experimenting.
It’s crucial to address IP protection and data privacy, especially when using cloud-based AI services. Ensure that your company’s intellectual property is secure and that vendor policies don’t allow for the retraining of models with your sensitive data.
Encourage experimentation and continuous learning. Share knowledge and experiences across teams to avoid repeating mistakes and speed up adoption. Creating internal communities around AI can foster collaboration and evangelize its benefits. Prioritize AI adoption, stay updated on industry trends, and empower teams to explore how AI can modernize their testing practices.
