“IDC: By 2028, GenAI-based Tools will be Capable of Writing 70% of Software Tests”
– source
As the leader of the AI Center of Excellence in a rapidly growing Quality Engineering and Software Testing company, I find this prediction both fascinating and invigorating. The evolution of Generative AI is no longer a distant dream; it is a reality unfolding before our eyes. Imagine a future where the majority of software tests are generated by intelligent systems, freeing human testers to focus on more complex, creative, and value-added tasks. This shift is not merely about automation– it is about augmenting human capabilities and transforming the software testing landscape.
Transforming Quality Engineering with GenAI
The modern Quality Engineering (QE) and testing lifecycle is multifaceted, involving various stages that ensure software reliability, functionality, and performance. Let us explore how Large Language Models (LLMs) and Generative AI tools are revolutionizing each phase.
Test Planning and Design
Requirement Analysis – GenAI tools can parse and understand requirements, generating comprehensive test cases and scenarios. For instance, the recent OpenAI’s GPT-4o (omnimodel) can analyze documentation to create detailed test plans, far better than before (like covering Risk Mitigation).
Test Case Generation – AI models can generate diverse and extensive test cases, covering edge cases that might be overlooked by human testers. And in the format we need. I bet that 60% or more of the functionality in any enterprise application is “standard” and “understood” by the inherent language processing prowess – (NLU – Natural Language Understanding) of GenAI.
Test Automation
Script Writing – GenAI tools can write automation scripts in various programming languages for popular automation tools (Selenium, Playwright, Cypress, and more) reducing the time and effort needed for script development. Assuming that you have a core framework in place, and if you are smart in teaching about it, the generated test scripts can align with it.
Code Maintenance – AI-driven tools can “self-heal” automation scripts by updating them based on changes in the application under test. This is not truly GenAI per se but if you see, we are still talking about “generating” the code updates from a UI model.
Test Execution
Dynamic Test Execution – GenAI can prioritize and execute tests based on risk assessment and previous test results, optimizing the testing process– the long awaiting promise of optimized regressions!
Real-time Analysis – AI tools can provide real-time analysis and reporting, highlighting defects and performance issues as they arise. With a human-in-the-loop, this will influence the quality we deliver in each phase/sprint.
Defect Management
Automatic Bug Reporting – GenAI can automatically log defects with detailed reproduction steps, making it easier for developers to understand and fix issues. The world need not live with poorly written bug reports anymore.
Predictive Analysis – AI can predict potential defect areas(clusters) in the codebase, enabling proactive testing and issue resolution– dynamic learning and shifting test focus, if you need a term for the phenomenon.
Performance Testing
Load Testing – GenAI can simulate various load conditions, providing insights into system performance under different scenarios. I have always believed that “Transaction Mix” can be generated by a thoughtful program, well the GenAI tools do it, as long as we give a proper instruction (prompt).
Performance Optimization – AI-driven tools can suggest optimizations to enhance application performance based on test results. For example, right updates to the configuration files or batch database ops from a session, and so on.
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… Security Testing, Accessibility Testing, or any other type of testing is lining up to take advantage of GenAI tools. The adoption is gaining momentum, with numerous industry leaders demonstrating its potential and effectiveness.
Let us embrace the future with optimism and curiosity, continually learning and adapting to harness the full potential of GenAI in our quest for excellence in software quality. The future is not just closer than we think; it is here, and it is exciting. – Link.
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Message? Embracing the Change, Upskill, and Evolve
The rise of GenAI in software testing might evoke concerns about the role of human testers. However, instead of viewing this advancement as a threat, we should see it as an opportunity. The integration of AI and GenAI into testing processes allows testers to upskill and evolve, focusing on strategic, analytical, and creative aspects of testing/QE that machines cannot replicate.
By mastering GenAI tools and methodologies, testers can enhance their expertise, making them indispensable in the new testing paradigm. The future of testing is a collaborative effort between humans and machines, where our human excellence drives quality innovatively.
Let us embrace the future with optimism and curiosity, continually learning and adapting to harness the full potential of GenAI in our quest for excellence in software quality. The future is not just closer than we think; it is here, and it is exciting.
“IDC predicting that GenAI will write 70% of software tests by 2028 is like saying the future has a superior fast-track lane – and if you, like me, believe it, be ready to get up to speed on it. And embrace the ride!” – Ashwin Palaparthi
