Answer:- The main challenge is the lack of trust in AI-generated test results. Testers often feel that AI may not cover all cases or produce false positives/negatives. To overcome this, Jason suggests building transparency into AI testing workflows, meaning testers should monitor and validate AI outputs, ensuring they align with business logic and user expectations.
How AI in Automation Testing Can Boost Your Career Success: Strategies from Jason Arbon | Testμ 2024
Answer:- Jason shared examples of testers who pivoted into AI solution architects and AI quality leads after mastering AI-driven testing tools. These roles typically involved overseeing AI implementation in testing environments, mentoring teams, and strategizing the use of AI to optimize broader testing processes. Such roles are highly sought-after, especially in organizations scaling their digital transformation efforts.
Answer:- Key skills include:
- Understanding AI/ML fundamentals: Even if you’re not developing AI models, understanding how they work will help you better implement them in testing.
- Familiarity with AI-based automation tools like Checkie.AI, Mabl, or Testim.
- Data analysis: AI in testing thrives on data. The ability to analyze, clean, and validate datasets is crucial.
From my point of view:-
Jason emphasized the importance of automating as much of your testing suite as possible using AI. When under pressure (such as handling 10x demand or being short on resources), AI can help maintain testing quality by covering critical paths, automatically updating tests, and scaling across environments. This approach ensures that the product can meet high demand without compromising quality, even in stressful situations.
I think AI offers a fast track for newcomers by helping them deliver results quickly. Those who can automate tedious tasks and bring efficiency improvements tend to get noticed. New testers who learn AI tools can accelerate their learning curve and demonstrate their ability to drive value, positioning themselves for early promotions or leadership opportunities
Networking in AI communities allows testers to stay up to date with the latest trends, tools, and challenges in AI testing. Attending events like Testμ or AI conferences provides opportunities to exchange ideas, collaborate, and even find mentors. Building these connections helps broaden your knowledge and visibility, making you more likely to be considered for promotions or new roles.
Jason often referenced tools like Checkie.AI, which uses AI to predict and identify defects early in the development cycle, and Testim, which supports codeless automation testing. These tools have helped organizations reduce manual testing efforts and accelerate their delivery timelines.
AI certifications can be a valuable asset, especially if you’re looking to stand out in the field. Jason Arbon emphasized that while certifications provide foundational knowledge, hands-on experience with AI tools and platforms is far more crucial. Certifications help signal your interest and commitment to learning AI, but it’s your ability to apply AI effectively in testing scenarios that will accelerate career advancement. Employers value practical skills over theoretical knowledge.
Testers should focus on understanding the fundamentals of AI and machine learning, as well as mastering automation tools. Jason pointed out that prompt engineering, data science basics, and familiarity with AI-driven testing tools like Checkie.AI or other codeless automation platforms are essential. Upskilling in Python or R for machine learning and working on model training would also be beneficial. Testers should understand not just the tools but also the “why” behind AI applications in testing to stay competitive.
from my point of view, One major challenge is the learning curve associated with AI tools, which can initially feel overwhelming. Jason noted that testers might struggle with AI tools generating excessive or irrelevant test cases. Overcoming this requires focusing on value-driven automation—tailoring AI outputs to your context. The ability to fine-tune AI for useful results sets you apart and opens up career advancement opportunities. Developing critical thinking around how AI enhances testing efficiency is key.
Yes, according to Jason, prompt engineering is becoming an essential skill for testers working with AI. Since large language models (LLMs) rely on prompts for output, the ability to craft precise prompts ensures more accurate results from AI tools. This skill directly impacts the quality and relevance of automated test cases, and it’s especially important in reducing the risk of generating unnecessary test cases that may hinder productivity.
I think cloud certifications (such as AWS Certified Solutions Architect, Microsoft Azure, or Google Cloud certifications) are beneficial, particularly for testers who want to transition into DevOps or work with cloud-based AI solutions. These certifications help you understand how to deploy and scale AI testing tools in a cloud environment, which is a critical component in modern testing strategies.
AI scripts can be more adaptable than traditional scripts, but they still require oversight, especially when faced with frequent changes in the application or environment. Jason suggested that testers should focus on fine-tuning their models rather than building from scratch every time. The key is setting up robust monitoring and retraining mechanisms to handle fluctuations. While AI can handle some variations automatically, human input is still necessary to refine outputs and ensure accuracy.
Jason mentioned Checkie.AI as a leading example of codeless AI-driven automation tools. Other notable options include Testim, Functionize, and Mabl. These tools reduce the complexity of script maintenance and allow testers to focus on higher-level test strategies rather than coding.
For testers moving into leadership roles, Jason recommended learning more about the strategic application of AI. This includes understanding ROI for AI tools, selecting the right tools for your team, and focusing on AI’s impact on business outcomes. Leadership also involves guiding teams through AI integration, so a solid understanding of both technical and managerial aspects of AI is critical.
Jason recommended a combination of traditional platforms like Coursera, edX, and Udacity for structured AI education. He also highlighted AI Testing tutorials from sources such as Test Automation University, ISTQB, and AI Testing newsletters or blogs like Ministry of Testing for staying updated on trends.
Thank you for the fruitful session, Here is the answer
For code-less automation testing there are many platform or tools but, I recommend LambdaTest. It offers an intuitive platform that allows users to create automated tests without writing code, making it ideal for teams with varying technical expertise. Additionally, it supports a wide range of browsers and devices, enabling extensive cross-browser testing.
Another excellent option is TestProject, which also provides a user-friendly interface for creating and managing tests with minimal coding required.
If you need further details or a comparison, feel free to ask!
I hope you all are well. I wanted to discuss how we can effectively introduce AI testing within our organization, given our reliance on legacy programming tools.
First, we should assess our current testing environment and evaluate existing tools to identify critical systems. Selecting a pilot project will allow us to experiment with AI testing without disrupting key workflows. It’s essential to choose AI tools that integrate seamlessly with our legacy systems and offer code-less automation features.
Training our team is crucial. We should provide workshops and encourage collaboration between testers, developers, and AI specialists to foster understanding. Gradual integration of AI testing alongside our existing tools will help manage risks, utilizing APIs or middleware as needed.
Establishing a feedback loop will enable us to gather insights on the effectiveness of the AI tools and make necessary adjustments. Additionally, securing management buy-in by presenting case studies that demonstrate the value of AI testing will be vital for support.
Finally, creating a long-term roadmap with clear milestones for AI testing adoption will guide our transition.
If you’d like to discuss this further, please let me know.
I hope this message finds you well. I wanted to share some effective strategies for testing the performance of AI models, especially given that responses can change with each prompt.
First and foremost, it’s essential to define clear metrics for evaluation. Key performance indicators such as accuracy, precision, recall, and the F1 score are critical for assessing the quality of the AI’s predictions. Additionally, measuring response time will help us understand the model’s efficiency.
Using a standardized dataset is also crucial. Leveraging benchmark datasets specifically designed for the type of AI model we are testing allows for consistent comparisons against established standards. Furthermore, creating a custom set of diverse test cases can help cover various scenarios, including edge cases that the model may encounter.
Conducting multiple trials will provide a more comprehensive performance profile. A/B testing can be particularly useful for comparing different versions of the model against the same prompts. Running a large number of trials will help us analyze variance in responses and gather statistically significant results.
In addition to quantitative metrics, qualitative analysis plays a vital role. Involving human reviewers to assess the quality of responses based on relevance and coherence can provide insights that numerical data might overlook. Gathering user feedback from those interacting with the model is also valuable, as their perspectives can highlight areas for improvement.
Testing consistency is important as well. We should evaluate how the model responds to slightly altered prompts to gauge stability. Utilizing random seeds during the model’s generation process can help us assess how consistent the outputs are under controlled conditions.
Furthermore, performance under load should not be overlooked. Stress testing the model will allow us to assess its performance when faced with high demand or multiple requests simultaneously. Monitoring latency and resource usage during these tests will provide critical insights.
Lastly, tracking drift over time is essential to ensure the model maintains its quality. Continuous monitoring will help identify any degradation as it is exposed to new data. Implementing logging for responses can aid in this analysis, allowing us to visualize performance metrics and trends effectively.
By combining these strategies, we can obtain a comprehensive understanding of our AI model’s performance and ensure it meets user needs effectively. If you have any further questions or would like to discuss this in more detail, please feel free to reach out.
Thank you for putting this important question in the lambdatest community,
I wanted to share some strategies for developing critical thinking skills among testers, particularly in the context of AI and automation.
To begin with, fostering a culture of continuous learning is crucial. Testers should take advantage of online courses and workshops that focus on AI technologies and testing practices. Gaining hands-on experience through real-world projects can significantly enhance their problem-solving abilities.
Promoting collaborative discussions is another effective way to encourage critical thinking. By holding regular brainstorming sessions, team members can share insights, challenge each other’s ideas, and develop a more comprehensive understanding of complex issues. Additionally, reviewing case studies of successful AI implementations can provide valuable lessons and inspire innovative solutions.
Instilling a curious mindset is essential for encouraging deeper inquiry. Testers should be motivated to ask questions and explore various viewpoints, which can lead to improved problem-solving. Moreover, honing data interpretation skills will enable them to make informed decisions based on insights derived from AI-generated data.
Finally, reflecting on past experiences and staying updated with industry trends will equip testers to adapt their strategies effectively. Implementing these approaches will not only improve individual skills but also enhance our team’s overall effectiveness in leveraging AI and automation.
If you have any further questions or would like to discuss these strategies in more detail, please don’t hesitate to reach out.