AI coding assistants have been claimed by many as tools that can significantly boost developer productivity and speed up the coding process. However, recent studies show that the actual impact of AI coding assistants on developer productivity may be limited. Let’s explore how AI coding assistants affect key metrics like coding time, bug rates, and developer burnout.
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Affect of AI Coding Assistants on Developer Productivity (Uplevel Study)
One of the most research on this topic was conducted by Uplevel, a company that provides insights from coding and collaboration data. They analyzed coding and collaboration data from around 800 developers over six months, comparing metrics from when developers did and did not use GitHub’s AI coding assistant, Copilot. Some key findings from their study:
1. Pull Request Cycle Time
No significant improvements in time taken to merge code using GitHub Copilot, indicating no boost in productivity.
2. Pull Request Throughput
Again, no significant gains were observed when using Copilot.
3. Bug Rates
41% increase in the number of bugs introduced when using Copilot compared to the original code.
4. Burnout
The amount of time spent working outside of regular hours decreased less for developers using Copilot compared to the control group, suggesting it did not help prevent burnout as much.
So as per measurable metrics like above, the Uplevel study found little evidence that AI assistants improve productivity or reduce burnout.
Mixed Experiences Reported from Companies
While some like Innovative Solutions reported 2-3x gains in productivity, studies found mixed results across organizations. Gehtsoft saw AI code harder to understand, debug and maintain compared to original code. Rewriting from scratch preferred over fixing AI code bugs.
Developer Perspectives on AI Coding Assistants
The CIO article also interviewed developers who had used AI coding assistants. Many reported that while these tools were helpful for basics, they struggled with more complex code. One CTO noted that the generated code was difficult to debug and improve, often requiring rewriting from scratch.
Some developers did see gains, but they closely reviewed the generated code instead of blindly using it. This suggests proper usage is key to realizing benefits instead of treating the tool as a “write code for me” button. Thus productivity boost depends on developer expertise and ability to effectively guide and review AI output.
Future Potential But Limitations Now
AI coding assistants show promise, but their current limitations have been acknowledged. Productivity metrics don’t capture all gains, and code quality is prioritized over speed. Experts agree AI won’t replace humans but augment them by automating routine tasks. As the technology matures, its true impact on developer workflows remains to be seen.
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