The AI Quotient™

The AI Quotient™ or ‘AIQ’ is a pioneering development representing the first time a company has been able to answer the question “How much of my code could be improved by AI?”. Designed to empower business leaders and IT professionals with a quantifiable AI-based score that identifies potential improvements within their codebases and the data to back it up.

How does the AI Quotient™ work and what will I see?

AIQ™ utilizes sophisticated AI technology to scan any company’s code base, assessing it for common coding pitfalls, quality issues, and structural inefficiencies. By evaluating these elements, AIQ™ assigns a dynamic score representing what percentage of the code could be enhanced with the aid of AI-driven tools such as co-pilots and coding assistants. This means if you’ve ever wondered how good your code is, or if any areas of your software could be improved – now you will have the answer!

The AI Quotient™

What types of issues will the AIQ™ uncover?

We’re not here to pass comment on coding styles or choice of language technology, The AIQ™ isn’t going to highlight subjective improvements, we’ll leave those down to the developers. What the AIQ™ will do is highlight widely accepted bad practices, coding issues, inefficiencies and security lapses. The issues detected are known to be handled well by modern AI coding models and services.

Bad Practices

There are many widely accepted bad practises and coding quality issues that development teams will accept. But it’s not always easy for developers to keep all of these potential issues in mind while working on a project. Over time it’s easy for these issues to present themselves, and eventually they can lead to problems like security issues, bad performance, technical debt and more.

Our AIQ analysis will find these issues across languages and present them all on a dedicated page. These will contribute to your overall AIQ percentage and can also be sent to your developers (or reviewed by them directly in our platform) to be fixed.

Security and Dependencies

As well as general coding bad practises such as insecure URLs, sensitive data found in source code, unsafe data deserialization and detected debugging code, we also include in our AIQ analysis any of the security issues we’ve already analysed and any of the outdated components we’ve detected.

We include these because it’s yet another change that we know the best AI coding models are great at improving (or helping your developers improve!). And it’s another reminder that these elements need to be looked at because they can lead to much larger security and technical debt problems if not sorted out!

Improvement by AI

In the world of AI, technology is changing at a rapid pace. At The Code Registry we use various AI tools internally while we work, and we also keep up to date with general AI advancements and new models and services.

So we know that the latest AI coding models are great at fixing common coding bad practises and helping to resolve known security issues. For example you could copy and paste an entire code file into a modern AI coding model and ask it to suggest improvements, and it will find a lot of the same issues you’ll find in our new AIQ dedicated analysis. It’s with this knowledge and experience that we give you the “Your code could be improved by AI” percentage

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Every business owner, budget holder or person responsable for their company’s software development has the same questions playing on their mind. 

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By signing up to The Code Registry you no longer have to have these worries as you know that you have an up to date replication of your code which you alone have access to. You have a simple dashboard which can help you explain your software to new partners or to investors and helps you to understand where issues might arise before they happen.

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