Generative AI has gone from being just a buzzword to proliferating rapidly through all tiers of industry. However, the most anticipated impact of the celebrated technology can be felt in the IT industry. In fact, the State of IT Report 2024 by Salesforce reveals some startling facts about the impact of gen AI on how IT and software companies function. According to the report, 57% of IT leaders believe gen AI will play a game-changing role in their organisations while an even bigger chunk of the leaders surveyed are looking to prioritise this revolutionary technology in the next 18 months. And it is within this newly developed and still growing gen AI ecosystem that new-age QA practitioners need to function. It can never be overstated that the only future-proof way to leverage the transformational advantages of gen AI in software development is by altering QA practices in tandem to keep pace.
Generative AI-driven coding will accelerate the pace of code generation and software development but without quality assurance and software testing able to keep up, much of the accrued ROI will be lost. QA testers require increased test coverage, intelligent test automation, and AI-driven test data management to match the increased velocity of code development. While there are many AI-led software testing solutions in the market, the task remains to check how gen AI will affect test coverage, productivity, risk mitigation, and test quality. We’ve documented the different changes that QA testers will have to account for and how AI-led intelligent software testing solutions can help them keep up.
Faster code generation will require faster testing and validation
One of the biggest changes, one that is already underway, is that of faster code generation with the help of gen AI tools. The market is flush with new coding tools that equip coders to complete coding tasks faster, allowing them to release code blocks for testing much earlier in the cycle. Moreover, the amount of code being generated has also gone up by a lot. A recent McKinsey study found that coders can complete their tasks twice as fast as before with new gen AI tools. This shift in the status quo has compelled testers to keep pace with them. But to do so, they too require AI-augmented testing solutions that use little to no coding to quickly create and execute test scripts. These tools enable all stakeholders to participate in the testing process while improving coverage and test quality.
Quicker feedback for code changes and updates
Faster coding doesn’t just increase the quantum of code itself but also has impacts downstream, one of which is the need to incorporate changes and updates to the ever-expanding codebase. But for teams with limited testing capacity, testing and validating each change within the sprint cycle can become a humongous task. As more code makes its way into the ecosystem, the more changes that need to be validated. The best way to resolve this issue is to condense the feedback loop.
With AI-based testing solutions, testers can find defects faster and address them before they barricade the CI/CD pipeline and impede work. With the right tool in hand, testers can quickly identify bugs, analyse UI, and automate test scripts, shortening the feedback loop and catalysing development.
Better test cases for higher test coverage
One of the biggest, if not the biggest, reasons behind poor test coverage is the lack of access to production-quality test data. With more code being pushed for testing, the lack of extensive test coverage will only magnify. Provisioning of representative test data that mimics production qualities without jeopardising compliance, privacy, and quality is a key tenet to fully reach the potential of gen AI-led coding.
AI-driven test data management enables testing teams to close coverage gaps within their test scenarios and accommodate faster feedback for the same. Moreover, intelligent test data management solutions also simplify maintenance and updating of test scripts. Intelligent pattern recognition, dynamic parameter adjustments, and vulnerability discovery reduces the need for manual intervention and speeds up testing cycles.
This is just the beginning of us evaluating the impact of gen AI on coding, testing, and QA practices. The multifaceted impact of this continuously evolving technology can only be matched with a robust, heterogenous no-code AI-driven test automation solution such as Avo Assure.
Avo Assure is an end-to-end testing automation solution that doesn’t just accelerate the testing cycle but also improves test coverage, productivity, resource utilisation, and software quality. With Test Design and Avo Genius, part of Avo Assure, you can instantly design test cases and update them as and when required. Combined with the pre-built 1500+ word keyword library, they let you create test cases much faster. Parallel execution and smart scheduling let you maximise the output from your current resources while the Upgrade Analyser lets you pinpoint the change in the code and test just that.
Integrate Avo Assure with Avo’s intelligent Test Data Management and Service Virtualization solutions and you’ll have the entire suite of test automation solutions that you require to future-proof your QA processes for the gen AI ecosystem. To learn more about Avo Assure, book a demo today.
