In recent years, the use of AI in development has shifted from science fiction to reality. One notable advancement was the introduction of GitHub Copilot, Microsoft’s AI pair-programming service, in the summer of 2022.
Following that, the arrival of ChatGPT 3.5 in November 2023 further ignited enthusiasm about AI in programming. A recent survey conducted by GitHub and Wakefield Research revealed that a staggering 92% of US-based developers are already utilizing AI coding tools both in their professional and personal work.
The survey, which involved 500 enterprise developers, found that 70% of programmers perceive AI as a valuable asset to their coding practices. Specifically, they highlighted how AI coding tools assist them in meeting performance standards by improving code quality, expediting output, and reducing production-level incidents. These tools have become integral to modern business IT, with only 6% of developers reporting exclusive use outside of work.
The rapid adoption of AI coding tools can be attributed to their positive impact on developers’ productivity. By enhancing code quality, facilitating faster output, and minimizing errors, these tools enable programmers to focus on resolving bugs and issues effectively.
But, Inbal Shani, GitHub’s chief product officer, added, “Engineering leaders will need to ask whether measuring code volume is still the best way to measure productivity and output.” The answer is no. Shani added, “Ultimately, the way to innovate at scale is to empower developers by improving their productivity, increasing their satisfaction, and enabling them to do their best work — every day.”
According to the survey, “Developers want to upskill, design solutions, get feedback from end users, and be evaluated on their communication skills.” In other words, generating code with AI is a means to an end, not an end to itself.
Emphasizing code quality over quantity has become a significant performance metric. However, there is concern that the availability of AI coding tools may encourage managers to prioritize code quantity over code quality, leading to subpar results.
Besides, as Mark Collier, OpenInfra Foundation COO, said at OpenInfra Summit in Vancouver, Canada, “The Python community is grappling with code reviews of AI-generated code, often because it’s crap, and the person ‘contributing’ it can’t explain it because they didn’t write it.”
While AI coding tools offer considerable benefits, they cannot address all challenges faced by developers. For instance, developers still spend a significant amount of time waiting for builds and tests, hindering their ability to learn new skills and tackle novel problems.
Nonetheless, AI and improved DevOps practices hold the potential to alleviate these issues, allowing programmers to dedicate more time to solution design and the creation of innovative features.
The survey indicates that developers are already leveraging generative AI coding tools to automate parts of their workflow, freeing up time for collaborative endeavors such as security reviews, planning, and pair programming.
It is important to note that AI is not meant to replace developers; rather, it aims to enhance their capabilities and overall satisfaction while making the programming process more efficient and productive, provided it is used appropriately.