Can you provide examples of how AI is augmenting or replacing traditional testing activities in your organization?
How to use AI to fix flaky automated tests? Need to pinpoint at functionality that is flaky, not only fix the tests.
How can AI be leveraged to improve test automation, and what are the potential challenges?
Which skills should the QA have in the future, and to stay on the market?
What key skills and competencies should quality engineers develop to succeed in an AI-driven environment?
We should focus on getting better at AI, machine learning, and making things automated. Get the hang of using AI in testing, and keep up with the latest tools and tech in quality engineering.
Put AI, automation, data analysis, and DevOps at the top of your list. Get to know how AI affects testing, learn to use tools that work with AI, and get better at coding (especially in Python or Java) is super important.
Testers can move into jobs like AI Test Engineers, Automation Architects, or Data Quality Specialists. These jobs are all about checking AI models, creating automation systems, and making sure the data in machine learning is solid.
- In 1 year, we’ll see wider adoption of AI-driven test generation and automated bug detection.
- In 5 years, most testing will be AI-augmented, with AI performing exploratory tests and anomaly detection.
- In 10 years, we could have self-healing systems and AI completely handling end-to-end testing.
Around 30-40% of the industry has AI integrated into QA processes, primarily in automation. Larger organizations are leading this shift, while others are slowly adopting AI-driven tools to improve efficiency.
AI makes tests faster by doing the same tasks over and over again and spotting problems quicker. But, AI might have a hard time getting the hang of complicated business rules or tricky situations, which means we still need people to step in for the finer details.
AI can:
- Performance Testing: Predict potential bottlenecks by simulating various loads.
- Accessibility Testing: Automatically detect accessibility violations based on guidelines like WCAG.
- Usability Testing: Use AI to analyze user behavior patterns and suggest improvements.
Engineers will need proficiency in AI/ML, data science, and automation frameworks. Familiarity with AI-driven testing tools and scripting languages like Python will be critical.
- Learn about AI/ML models and their limitations.
- Get comfortable with automation and CI/CD pipelines.
- Develop problem-solving and analytical skills to understand AI decision-making.
You can start with online courses on platforms like Coursera, edX, and Udemy for AI basics. Specialized certifications in AI testing are also becoming more available.
Testers who get good at AI and automation will be super in demand. Jobs will move from doing things by hand to ones that use AI and automation a lot. Testers will need to get better at tech and be comfortable with AI tools.
It’s tough to make AI that’s totally free of biases because AI learns from data that can be biased. But, teams can reduce bias by using a mix of training data and always checking AI’s decisions.
QA teams are now putting more effort into automation and testing with data. They also have to make sure AI models are checked for bias, fairness, and accuracy, which means their job has grown to include more than just making sure things work.
AI can be taught by giving it data that’s specific to the business and using reinforcement learning. Working together between business analysts and developers is key to coming up with useful test cases and goals for the AI.
- Blockchain: Secure and immutable testing environments.
- Quantum Computing: Potentially transforming computational speed for testing.
- DevOps & Continuous Testing: Enhancing agility in testing workflows.