Quality is the most critical aspect of a digital product. It builds the user’s trust, increases adoption levels, generates greater engagement, and helps enhance the reputation of the organization that puts the application online.
While for Making Sense quality is an essential part of our projects, the emergence of generative artificial intelligence tools based on neural network models, such as GitHub Copilot (owned by Microsoft) allows us to take our task to a higher level.
We are committed to continually exploring new technologies and how to apply them to gain efficiencies, increase profitability, generate new business models, or optimize the user experience. But the same things we use to enable our customers to do things better, we also adopt internally with those same goals in mind.
Speed, efficiency, focus
Overall, the advantages of using Copilot in software development are undeniable. Its primary value is that it saves time in routine matters. Data from GitHub itself states that it speeds up repetitive tasks by 96%. For example, when a line of code is repeated numerous times throughout a block or has some variations that are not significant, it will be enough to write it once.
Suppose there is a doubt about a sentence. In that case, it will not be necessary to leave the application and go to the search engine to look for the correct form. With a simple tap, Copilot will make a suggestion and even autocomplete the line. Even if the question is more complex, you can consult the chat in natural language, and the answer will appear in no time.
Like any innovation, it requires users to take some precautions. Copilot’s proposals are not always correct. The experience and knowledge of the Automation Engineer are vital to making the most of the tool’s power and using the suggestions that work.
For the Automation Engineer, the transition is a smooth one. The configuration and subsequent learning curve are solved in just minutes. It is straightforward to use and is very flexible in that each suggestion also shows the options to accept it, edit it for improvement or reject it.
An assistant to set up the tests
What happens when we use Copilot for QA? In principle, it allows us to significantly reduce the time needed to generate automated tests from days to hours. With that surplus, the quality expert can concentrate not only on implementing that test but also on developing new tests that evaluate negative cases, extreme situations, or variants of the same case but with other input data.
Let’s suppose the test is to verify that the login can be done successfully (what is known in the testing area as “happy case”). With the time saved in test generation, the Automation Engineer can evaluate negative alternatives, such as the different ways the login can fail, and test them all.
From the product point of view, therefore, we are talking about a more productive development team, which always translates into lower costs or, doing more with the same budget. And, of course, a significant improvement in the end quality.
The digital race is extremely competitive. A good co-driver helps us get to the finish line faster. But if the person behind the wheel is experienced and knowledgeable, we have everything it takes to reach the top spot on the podium.