How can you balance the use of AI and human testers in your testing team?

AI tools are ideal for repetitive and high-volume tasks, such as running thousands of test cases or scouring logs for error patterns. On the other hand, human testers are good in areas where intuition, contextual understanding, and empathy are key – such as user interface testing and understanding the end-user perspective. A successful strategy involves using AI to reduce human efforts, not replace them. For instance, AI can flag areas of concern or patterns, which human testers can then investigate in depth.

Achieving an optimal balance between AI and human testers in a testing team requires a strategic approach. AI can be leveraged to automate repetitive tasks and expedite routine tests, freeing up human testers to focus on more complex aspects like contextual understanding and user experience evaluation. Close collaboration ensures the unique advantages of each resource are maximized, guaranteeing comprehensive test coverage and a thorough assessment of software quality. This synergy between AI and human testers is essential for effective and efficient testing processes.

In the vast landscape of software testing, AI emerges as a transformative toolset, promising efficiency from test generation to result analysis. This game-changing potential, however, comes with limitations. Not all testing aspects suit AI. Human involvement remains crucial, and the reliability and accuracy of AI tools are paramount concerns. While AI can revolutionize testing, it's not a silver bullet. Recognizing its strengths and limitations, and incorporating human oversight, ensures a robust and reliable testing landscape. In summary AI is a tool we in the Testing industry need to learn how to use this new tool. Not only how to use it but also test the AI models to be sure they are safe.

As AI is not mature enough, we have to follow the principles "Trust but verify" when it comes to Test design or test execution or defects identification. lets take a look at test design phase where there are number of AI tools available which can generate manual test cases based on either requirements/user stories or established flowcharts of the workflow scenarios. In both cases, there is dependency on the input which in our case is (requirements and flowcharts). if either requirements or flow chart are not fully defined and missing certain information you will have gaps in test case generation and as a result your coverage will be impacted. Thats why as a Tester we need to assure that input and outputs by AI is properly validated.

Balancing the use of AI (Artificial Intelligence) and human testers in a testing team is crucial to harness the strengths of both and achieve effective and efficient testing processes. Here are some thoughts to strike a balance: - Identify Appropriate Use Cases - Automate Repetitive and Time-Consuming Tasks - AI-Augmented Testing - Continuous Learning and Adaptation - Invest in training for both AI and human testers to enhance their skills. - Define Clear Roles and Responsibilities - Focus on Exploratory Testing by Human Testers - Feedback loop By combining the strengths of AI automation with the creativity, intuition, and domain expertise of human testers, testing teams can achieve a comprehensive and effective testing strategy.

"AI will not replace testers but rather evolve their role... Shift towards Exploratory testing: AI can take over the mundane tasks, allowing testers to focus on Exploratory testing, where Creativity and Critical Thinking are crucial. This involves manually exploring the software to identify unexpected issues and edge cases that AI might miss.

As the trust level on AI increases, the role of human testers continue to shift from execution to validation. This still brings in more efficiency and a strong move to reduce the number of human testers. That may not necessarily mean job loss for testers but an increased productivity.

AI follows principles "garbage in, garbage out" that is why it is crucial to verify and validate input which is typically produced by humans and output produced by AI for accuracy and completeness purposes.

AI won't replace human testers but can significantly enhance their productivity. Consider creating custom applications or utilizing coding assistants like Codium or GitHub. AI tools can aid testers in several ways: 1. Boosting the efficiency of designing test automation scripts. 2. Cutting down on the effort needed for test maintenance. 3. Assisting in decision-making through predictive analytics. 4. Enhancing and optimizing the quality of test automation code. 5. Facilitating the conversion of test automation scripts from one language to another.

Yes, it is quite useful. You can get some repetitive work done from AI without any spelling mistake 😉 . It can generate some quite interesting scenario for a specific case if you want to explore more about a feature.

An AI tool cannot completely replace human expertise; its capabilities are limited to a certain extent. It can be used to enhance and improve processes, but it cannot entirely replicate the depth of human knowledge and understanding. AI is most effective when used as a tool for assisting humans in tasks, providing insights, and facilitating optimization.

How do we test an AI ? What are the tools that we should use to make sure they are safe ? Recently I participated in a project of a LLM and in the beginning it would answer any prompt. Whatever you ask the LLM would have a response and in edge cases it would not be considered safe, legal or the correct answer. Relying only on human feedback is not an option. Using other LLMs to assert can also impose risks. Where are the Voight-Kampff test cases for this new emerging technology ?