Five questions to answer before adopting AI-generated code practices

In the digital era, the ability to ship code faster than competitors creates an almost incalculable advantage. It allows businesses to introduce new and better features, be more responsive to customer needs and market trends, and reduces the resources needed for each project. It’s no wonder then that the prospect of generative AI coding assistants taking on significant amounts of the burden of coding is creating such excitement. When used effectively, these tools have the potential to halve the time needed for the average software development project.

However, if AI assistants are deployed without due diligence, they can create more work, not less, for overstretched development teams. Every line of code must be rigorously tested, secured, and remediated before it goes into production. A sudden and dramatic increase in the amount of code being created therefore places an unmanageable burden on developers, especially since research has found that around 40% of copilot-created code contains bugs. As a result, poor implementation of generative AI can end up actually increasing developers’ workload, leading to reduced productivity and burnout.

Check, test, verify