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AI to Enhance Your Quality Throughout the Development Cycle

By Danny Paradis: Solution Architect specialized in user interface testing automation.

March, 21 2024

Today, I want to invite you to a captivating reflection on the transformative power of Artificial Intelligence (AI) in the world of software development. AI goes beyond merely transforming application development methods; it redefines the notion of quality at each stage of the process. Let's explore together three uses of AI in the development cycle: quality in user interface definition, quality in test coverage, and intelligent test maintenance. 

User Experience and Accessibility at the Heart of Design

Imagine the scene... AI tools that analyze thousands of designs to suggest the best user interface (UI) for you... UIs that are not only aesthetically pleasing but also optimized to offer an intuitive user experience (UX). Systems that evaluate the accessibility of your design in real time, ensuring your application is usable by everyone, without exception. Isn't that a promising and revolutionary perspective for digital product development?


To illustrate my point, here's an example of a discussion I had with ChatGPT on my mobile phone.

Danny: You are a UI specialist, tell me what you think of this screen sketch for my new software.

ChatGPT: The screen sketch you shared for your new software appears to be an initial wireframe showing the user interface (UI) for uploading and generating content. Here are some observations:


· Clarity: The elements are clearly defined, helping to understand the structure and function of each section.

· Simplicity: The interface is simple, which can make the user experience less intimidating and more accessible.

· Hierarchy: There seems to be a visual hierarchy, with the "Drop or Upload your image or text" as a central point, but this could be improved by adjusting the size and placement to guide the user more effectively.

· Labeling: Labels like "generate REACT code," "generate API Swagger," "generate User Flow," and "generate Unit test" are specific and direct, which is good for understanding the functions.

· Interactivity: It is assumed that each box represents a button or interactive area, but this could be made more explicit, perhaps by adding visual conventions like raised buttons or distinct drag-and-drop areas.

· User Flow: The overall flow seems logical, but it could be helpful to show how users move from one step to another.

· Responsive Design: Consider how this will adapt to different screens or devices.

· User Feedback: Think about integrating elements that provide interaction feedback to users, like animations or confirmation messages.


In general, for an initial sketch, it's a good start. The next step would be to develop these wireframes into more detailed prototypes, incorporating feedback on ergonomics and user experience (UX) to test with real users.

source: Chat GPT

The implementation of software quality has undergone a profound transformation thanks to AI. Now, AI-powered tools facilitate the definition of relevant test cases, unit tests self-evaluate to ensure optimal code coverage, and end-to-end (E2E) tests dynamically adjust to encompass complex scenarios. With AI, we benefit from faster and smarter testing.


Here's an example of a fictional workflow ("flow") I wrote for a candle store. To do this, I used Mermaid, a fantastic tool for visually describing a workflow.

Subsequently, I asked ChatGPT to write a test table based on the "flow."

Not being satisfied with the response, I asked it to optimize the test cases.

This is just a small example of how AI can help us in the development of tests.

Towards an Era of Simplified Maintenance

And what if I told you that test code can self-repair? Thanks to AI, this is a conceivable reality. How? Through algorithms that learn from each bug and self-correct, thereby reducing maintenance time and increasing test reliability.


For example, when a change is made to the graphical interface, the AI might not find the button, but it will try to find its equivalent based on other attributes (role, text, or coordinates) to replace it during execution, preventing a false positive. Obviously, the test AI will produce a report of the changes that you can accept or reject to maintain control over the tests. Many commercial tools already offer this functionality.


Artificial Intelligence is not just an addition to our software development toolbox; it is a companion guiding us toward a future where quality, efficiency, and innovation converge harmoniously. I am firmly convinced that integrating AI into our development processes represents the next major advance towards software excellence.


Finally, for those concerned about privacy issues, know that it is entirely possible to have your own AI on your workstation, eliminating this concern. Additionally, the advantage of using your own AI lies in the possibility of continuously perfecting it! Stay tuned for my next article! You will discover how to equip yourself with your own AI on your workstation.


So... Have I piqued your interest in exploring the countless possibilities offered by Artificial Intelligence in development cycles? 


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