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Coding on Copilot: 2023 Data Suggests Downward Pressure on Code Quality

Including 2024 projections for specific code reuse

GitHub and other sources have reported more than 50% of developers adopting AI Assisted-development during 2023. What these sources haven't reported is how the composition of code changes when AI is used.

We examine 4 years worth of data, encompassing more than 150m changed lines of code, to determine how AI Assistants influence the quality of code being written. We find a significant uptick in churn code, and a concerning decrease in code reuse.

Code Churn by Year
Churn by year

Start reading to learn:

  • What are the three significant changes since Copilot's introduction?
  • What do Technical Leaders need to be on the lookout for 2024?
  • How can you measure the impact of AI on your team's code quality?
Coding on Copilot
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2023 marked the coming out party for GitHub Copilot. In less than two years' time, the AI programming assistant shot from “prototype” to “cornerstone,” used by millions of developers across hundreds of thousands of businesses. Its unprecedented growth defines a new era in “how code gets written.”

GitHub has published several insightful pieces of research on the growth and impact of AI on software development. Among their findings is that developers write code “55% faster” when using Copilot. This profusion of LLM-generated code begs the question: how does the code quality and maintainability compare to what would have been written by a human? Is it more similar to the careful, refined contributions of a Senior Developer, or more akin to the disjointed work of a short-term contractor?

To investigate, GitClear analyzed approximately 153 million changed lines of code, authored between January 2020 and December 2023 [A1]. This is the largest known database of highly structured code change data that has been used to evaluate code quality differences [A2]. We find disconcerting trends for maintainability. Code churn -- the percentage of lines that are reverted or updated less than two weeks after being authored -- is projected to double in 2024 compared to its 2021, pre-AI baseline. We further find that the percentage of "added code" and "copy/pasted code" is increasing in proportion to “updated,” “deleted,” and “moved” code. In this regard, code generated during 2023 more resembles an itinerant contributor, prone to violate the DRY-ness of the repos visited.

We conclude with suggestions for managers seeking to maintain high code quality in spite of the forces currently opposing it.