The proliferation of Artificial Intelligence (AI) has reverberated throughout every industry, and the field of software testing is no exception. As AI automates repetitive tasks and generates test cases, some people are worried that human testers face a bleak future. Despite these fears, AI can be powerful partner, rather than a replacement for Quality Engineers. This is how Quality Engineering will not only stay relevant but flourish in the age of AI:
Embrace the automation revolution to save time
Repetitive tasks like regression testing are prime candidates for AI automation. It can handle day-to-day test planning and test execution tasks which will give you more time to focus on engineering quality into the entire lifecycle. This allows you to focus more on:
- Designing high-impact test plans: shift your focus from writing individual test cases to crafting comprehensive end-to-end test scenarios.
- Exploratory Testing: with AI managing the basics, you can delve deeper into uncharted territory, using your creativity and intuition to uncover hidden bugs and usability issues.
- AI optimization: become an expert in AI Testing and Quality Engineering tools by understanding how they work, their limitations, and how to interpret their results so that you can expand and leverage AI more effectively across the Quality Engineering lifecycle, saving time, and money. You can then use this knowledge to refine your Quality Engineering approach, your test automation strategies and ensure AI is working for you, not against you.
Become a master of non-functional testing
AI excels at automating functional testing, but areas like performance, usability, and security still heavily rely on human expertise. AI can help with identifying anomalies, generating scripts, triaging defects and assessing outcomes for security and performance testing. It is becoming more complex to identify the issues for performance degradation; end-to-end test cases are the backbone for performance tests, and there is a plethora of low code/no code tools to aid you.
Two of the key areas of non-functional testing that can be automated successfully include:
- Performance testing: as software becomes more complex, performance bottlenecks become harder to identify. Develop a keen eye for performance optimisation and ensure applications deliver a smooth user experience under load.
- Usability testing: AI can struggle to understand the nuances of human interaction. Refine your user empathy skills to identify areas where the user interface might be confusing or frustrating. From a coding and function view, the tests will pass, but from an assurance perspective it is about the user experience and the role of a Quality Engineer is find and detect these anomalies.
The human touch and what AI can’t replicate
AI excels at data analysis and pattern recognition, but it lacks the critical human qualities that are essential for effective Quality Engineering. Some of the things it often struggles with include:
- Creativity: AI is not great with “outside the box” thinking. It’s your job to dream up innovative test scenarios that push the boundaries of the software.
- Judgment: AI can identify issues, but it can’t always determine their severity or prioritise them effectively. Your experience and understanding of the project context are crucial for making these decisions.
- Communication: clear and concise communication is vital for collaborating with developers and stakeholders. AI can’t replace your ability to explain complex technical issues in an understandable way.
The future of quality engineering through a collaborative approach
Software testing’s dynamic AI and human partnership could usher in a golden age of software development. Here’s what this collaboration will look like:
- AI-powered test generation: AI will be used to analyze user behaviors, code patterns, and past test results to generate more efficient test cases, which will free developers to focus on higher-level testing strategies.
- Human-in-the-loop Quality Engineering: AI can automate test execution and initial bug detection. You’ll then step in to analyze complex bugs, prioritize issues, and determine root causes.
- Continuous learning: humans and AI can learn from one other. AI data algorithms will be given insights on which to train garnered from human testing, while experienced QA testers can work smarter when they use AI-generated data to positively iterate their test strategies.
Final thoughts
When you embrace AI to enhance your skillset and free yourself to channel your unique experience and strengths, you will ensure that Quality Engineering continues to be a vital element of the software development lifecycle, enhanced by AI. Software testing has a bright future in which humans and AI will deliver exceptional software quality in partnership.
