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6 ways generative-AI can save companies on software testing spending

Tal Barmeir

By Tal Barmeir, CEO, BlinqIO

It’s no secret, generative-AI is rapidly transforming the realm of software testing, offering groundbreaking solutions that not only reduce costs for companies but also elevate efficiency, quality, and adaptability in the ever-evolving domain of software development. There are many ways in which I foresee generative-AI helping to curb costs for enterprises of all shapes and sizes, here are just a few.

  1. Reduction in headcount requirements for testing

The advent of generative-AI in software testing signifies a monumental shift in how testing operations are conducted. With its ability to autonomously handle test requirements, including the creation and maintenance of automation scripts, I believe generative-AI is a game-changer. Such innovation has the potential to reduce human tester headcount by an astonishing 90 percent. Such a dramatic decrease streamlines operations, substantially minimizing labor costs, and revolutionizing the traditional structure of testing teams. By automating routine and repetitive tasks, generative-AI allows human testers to focus on more complex and creative aspects of software testing, thereby enhancing the overall efficiency and effectiveness of the testing process.

  1. Continuous Operation Removes Context-Switch Costs for Developers

One of the most significant advantages of employing generative-AI in software testing is its ability to operate continuously, without the constraints of human work schedules. Unlike human testers who require breaks and have limited working hours, AI systems can function around the clock. This constant operation is crucial in eliminating costly context-switching for human developers. By providing immediate feedback and uninterrupted testing, generative-AI ensures a seamless and efficient development process. This 24/7 operational capacity is particularly beneficial in agile and DevOps environments, where continuous integration and continuous delivery (CI/CD) are critical.

  1. Multilingual Testing Reduces Costs for global applications

In today’s globalized digital world, the ability to test software in multiple languages is paramount. Generative-AI’s linguistic versatility is a massive plus, especially with cost efficiency. It enables swifter testing of global websites and applications in numerous languages, including but not limited to Korean, French, German, and English. This capability significantly diminishes the need for multilingual testing teams, thereby making global outreach more cost-effective and streamlined. It is also particularly beneficial for organizations aiming to expand their market reach globally, ensuring that their software products are accessible and functional in various linguistic and cultural contexts.

  1. Scalable Testing Resources Eliminate Waste from idle testers

Generative-AI brings unparalleled flexibility in scaling testing efforts to meet varying demands. Organizations can effortlessly increase their testing capacity manifold during critical periods such as pre-release phases and equally swiftly scale down immediately after. Such dynamic scalability ensures a lean and efficient use of resources, optimizing costs, and ensuring that the right amount of testing power is available at the right time. I think this scalability is especially beneficial in managing the ebbs and flows of software development cycles, allowing for an agile response to changing requirements and timelines.

  1. Uncompromised Quality Reduces Support and Launch Costs of Buggy Software Products

Another key benefit of integrating generative-AI into software testing is the assurance of releasing high-quality products. AI’s meticulous and thorough testing approach leaves virtually no room for errors, making it more cost effective. This relentless pursuit of perfection maintains the highest standards of product excellence, crucial in a market where customer expectations are constantly escalating. The AI-driven testing approach ensures comprehensive coverage, detecting issues that might be overlooked in manual testing, thus significantly reducing the risk of post-release bugs and enhancing user satisfaction.

  1. Customizable Tester Profiles Ensure a Positive User Experience for All

    Generative-AI’s versatility allows it to adapt to various testing scenarios, ranging from simulating novice user experiences to handling expert-level complexities. This customizable approach enables diverse and thorough testing, adaptable to any software application’s unique needs and user demographics. Such adaptability is particularly beneficial in tailoring the testing process to specific target audiences, ensuring that the software is user-friendly and meets the expectations of its intended users, which are typically corporates with budgets.

To conclude, I firmly believe the integration of generative-AI into software testing represents a paradigm shift. By significantly reducing costs while enhancing the efficiency and quality of testing, AI-driven solutions are emerging as game-changers in the competitive world of software development. The transformative impact of generative-AI extends beyond mere cost savings, elevating the overall standard of software testing and paving the way for more innovative, reliable, and user-centric products.

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