The generative AI space has been hot hot hot recently, and G2 has been hard at work expanding our taxonomy to represent its emerging markets.
We’re proud to introduce our AI Code Generation category, which represents a nascent but buzzing technology for automating development work.
AI code generation software uses artificial intelligence (AI) and machine learning (ML) to produce one or more lines of code based on a user prompt.
These tools leverage large datasets to generate quality code that follows both general best practices and governance laid out by the user’s organization. Users describe the function they want coded via plain text, and AI code generators provide code that fits the desired outcome.
AI code completion is a rising trend, and the automated capabilities it brings to the table can drastically reduce development time without sacrificing code quality.
When implemented with a user’s existing development environment, AI code generation tools act as a virtual coding assistant. Using large datasets as well as custom input to determine best practices, these AI companions can generate code based on the start of a line or a completely codeless prompt.
Some AI code generators are accessed via an online chat window, whereas others integrate directly into existing integrated development environments as full AI code assistants. On the more complex end of the spectrum, some of these tools can carry out their own forms of secure code review and software composition analysis. Using G2’s new category, buyers can compare some of these solutions and discover which is right for their team’s needs.
As with every category of software, it’s not all flowers and robot best friends when it comes to AI code generators. Because many of these tools can generate huge chunks of code based on short prompts, peer code review is extremely important. Code generated by AI should not be pushed to production without oversight to ensure security, quality, and compliance standards have been met.
And if something does turn out to be broken, bug tracking may get messy. With no human author to own the broken code, hunting down what’s causing the error could take more time than usual. That may put a wrench in the time saved by using AI code generation in the first place, and potentially lead to major headaches.
AI code generators are still quite new, with solutions varying from each other in both form and breadth of functionality. Keep an eye on this space as the dust settles and key leaders emerge. Regardless of how things shake out, it’s clear that AI code generation will continue to define itself as a productivity-boosting boon in the development process when used wisely.
AI is transforming every industry. Learn more about the future of writing through the lens of AI.
Edited by Sinchana Mistry
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