The Business Impact of Efficient Testing
Testing is a critical stage in the software development cycle. Mainly, because it ensures a streamlined user experience, one of the keys for appealing and profitable products and services. For businesses, ensuring a top-notch testing process can potentially bring a peak in value creation:
- QA makes sure that all functionalities of an application are user-friendly, and working properly and smoothly, which translates into satisfied and engaged users.
- UI testing ensures compliance, helping companies reach market accessibility standards, reducing legal risks, and protecting the company from potential fines of thousands of dollars.
So, can you imagine the perspective for businesses when Artificial Intelligence made its entrance into the game? Dive into this article to discover how AI-powered tools optimize testing, allowing companies to accelerate time-to-market, reduce costs, and release high-quality products faster.
How AI Optimizes Testing: Main Benefits to the Business
At Making Sense we’ve been exploring, trying, and vetting AI-powered testing tools for a long time now, and we’re convinced that they are great tech allies for businesses to strengthen their competitive edge, meet evolving market demands faster and better.
Let’s make a note here. The AI universe is a broad field with different branches such as Machine Learning, Gen-AI, Generative Adversarial Networks, and Large Language Modeling (LLM). For software development processes, what we use the most is LLM, which is process automation through AI. For example, ChatGPT and Copilot -tools we extensively use in our projects- are great examples of Large Language models.
Here are their main benefits from an operational and business point of view:
- Increased efficiency: For example, we ask ChatGPT to generate test cases through a simple prompt (the context given to the algorithm to create the intended output). That way, we avoid doing a repetitive task, cutting down testing time and resource use, to focus on more strategic things like executing the cases themselves.
- Scalability: When we automate tests, we use Github’s Copilot a lot, which works by consuming ChatGPT’s engine. It automatically predicts the next code you’ll need when you’re coding tests. That not only saves you time but allows you to scale, implementing more tests.
- Faster talent onboarding: AI-enabled tools make the learning curves of team members shorter, accelerating project onboarding, improving employee experience, and reducing the impact of labor market rotation.
- Cost-effectiveness: All benefits above are also catalysts of return on investment for companies, saving operational costs and speeding time-to-market.
The Making Sense Approach: Enhancing Business Value with AI-Powered Testing
At Making Sense, we have extensive experience including AI-powered tools in:
- Testing processes.
- A variety of projects that go from framework migration for the app of an US top company in the health insurance industry.
- The development of a back-office solution for Hanwha, the global leader in video surveillance systems.
Along the road, we’ve confirmed its power to optimize timeframes, facilitating scalable processes for building long-term growth and a sustainable competitive advantage.
Metrics speak for themselves:
- 70% of time saved migrating automation frameworks.
- 30% of time saved developing new test cases.
Our secret formula? Here are two aspects we take very seriously when implementing AI in testing.
Data Security
A very important thing to keep in mind when working with AI-based tools is that you are exposing your customer data and business workflows—e.g. when specifying context to create use cases. So, when choosing a tool, you are professionally compelled to do security analysis.
For this purpose, at Making Sense, we formed a group of experts dedicated to vetting and approving the tools we use before diving into a new project. Frequent presentations are held to the entire team to keep everyone informed on the state of the market.
Additionally, we’ve created a company-wide policy—our own AI playbook—with guidelines on how to proceed with AI tools as well as a list of pre-approved tools in the company. Needless to say, we always ask clients for previous authorization to use them.
Professional Criteria
It’s essential to remember that the benefits of using AI tools are leveraged when professionals implement them conscientiously. Tools that predict code can be very useful but they make mistakes—and criteria to analyze the output is mandatory to avoid defective code.
Unlock Long-Term Value with AI in Testing
In a nutshell, welcoming AI in Testing isn’t just about testing faster but about providing long-term value for the business. At Making Sense, we have a foundational commitment to innovation, always looking for new technologies, trends, frameworks, and processes to make our work more efficient.
Want to unlock business growth, reduce costs, and stay innovative in an increasingly competitive market? Contact us here. You may complete a form so we can contact you directly or schedule a 30 minute meeting to discuss how your company can boost its ROI with IA-powered testing.