WorkDifferentWithAI.com Academic Paper Alert!
Written by Sida Peng, Eirini Kalliamvakou, Peter Cihon, Mert Demirer
Category: “AI for IT”
Article Section: AI Development and Operations; AI-Assisted Programming
Publication Date: 2023-02-13
SEO Description: “AI improves developer productivity: Study on GitHub Copilot shows faster task completion with AI programming assistance.”
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The academic paper titled “The Impact of AI on Developer Productivity: Evidence from GitHub Copilot” conducted by Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer, investigates the role of generative AI tools in enhancing human productivity. The authors carried out a controlled experiment using GitHub Copilot, an AI pair programmer. Software developers were tasked with implementing an HTTP server in JavaScript in the quickest possible time. Findings reveal that the group aided by the AI pair programmer completed the task 55.8% faster than the control group. The paper suggests the potential of AI pair programmers in assisting individuals transition into software development careers.
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Here are my key takeaways from this academic paper:
- The paper presents results from a controlled experiment evaluating the productivity impacts of GitHub Copilot, an AI pair programmer that suggests code snippets and functions.
- 95 professional developers recruited through Upwork participated in the study. They were randomly assigned to a treatment group with access to GitHub Copilot or a control group without access.
- Both groups were tasked with implementing an HTTP server in JavaScript as quickly as possible. The treatment group completed the task 55.8% faster on average than the control group (71.17 vs 160.89 minutes).
- The results suggest GitHub Copilot can significantly increase developer productivity for certain tasks. The study found particularly large productivity gains for less experienced developers, older developers, and those who program more hours per day.
- This is one of the first rigorous productivity studies on generative AI tools for software development. The standardized programming task allowed precise measurement of productivity impacts.
- Limitations include examining a narrow programming task with a small sample. Effects may vary across tasks, languages, and professional settings. The study did not evaluate code quality impacts.
- If the productivity results generalize, it suggests major economic implications, like cost savings and GDP growth. But the job impacts are unclear – will demand for developers change? How will work be reorganized?
- Overall, this rigorous study provides early evidence that AI generative tools can meaningfully improve productivity for certain development tasks. But more research is needed on implications across tasks, quality, and broader labor market impacts. Understanding these effects can inform responsible deployment.
In terms of commercial applications, the significant productivity gains demonstrated suggest major cost and time savings potential for software firms from incorporating AI developer assistants like GitHub Copilot. The results also highlight opportunities to use these tools to make software development more accessible for novice or transitioning developers. More research is still needed to fully optimize use cases and mitigate risks.
Keywords
AI impact, Developer Productivity, GitHub Copilot, Generative AI tools, Software Engineering
Author’s Abstract
Generative AI tools hold promise to increase human productivity. This paper presents results from a controlled experiment with GitHub Copilot, an AI pair programmer. Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible. The treatment group, with access to the AI pair programmer, completed the task 55.8% faster than the control group. Observed heterogenous effects show promise for AI pair programmers to help people transition into software development careers.