GitHub conducted a survey among developers on how they use generative AI tools when creating software.
The survey involved 2,000 developers equally from the US, Brazil, India, and Germany.
The survey was conducted online from February 26, 2024 to March 18, 2024.
The survey defined AI tools for coding as “any developer tools that use generative AI and LLM to provide engineering assistance throughout the software development lifecycle.”
The majority of respondents were software engineers, developers, and programmers, with a small number of data scientists and software designers also included to get a more complete and diverse view of AI’s impact.
Survey respondents reported that AI helps them work more productively, using the saved time to design systems, collaborate more, and meet customer requirements better.
These findings suggest that individual use of AI is not enough. Organizations need to operationalize AI throughout the software development lifecycle to boost collaboration, creativity, and modernization.
AI doesn’t replace human jobs — it frees up time for human creativity.
The growing AI wave in software development
, a finding consistent across all four countries. However, a smaller percentage said their companies actively encourage AI tool adoption or allow the use of AI tools, varying by region. The U.S. leads with 88% of respondents indicating at least some company support for AI use, while Germany is lowest at 59%. This highlights an opportunity for organizations to better support their developers’ interest in AI tools, considering local regulations.
More than 97% of respondents reported having used AI coding tools at work. And the proportion of employers that either “actively encourage” or “allow” its use ranges from 59% in Germany to 88% in the US.
To maximize the benefits of these tools, organizations should have a roadmap, a clear strategy, and policies in place to ensure wider adoption happens through building trust and driving measurable performance metrics.
Software development teams recognize more benefits from AI coding tools than previously reported. Some of these include building more secure software, improved code quality, better test case generation, and faster programming language adoption. This ultimately translates into time savings that they could use for more strategic tasks.
The challenges behind organizational adoption of AI coding tools
Adopting AI coding tools at the enterprise level means that companies need policies that facilitate using these tools in workflows, while also considering factors like process changes, governance, and compliance.
While developers seek workflow improvements, leaders must also consider broader strategic goals and regulations.
Nearly half (48%) of respondents working at organizations that are actively promoting AI tools reported that their toolchains are “easy” to use. In contrast, significantly more (65%) of respondents from organizations with a neutral stance on AI use described their toolchains as complex.
The benefits of AI coding tools
The survey identified several key benefits that respondents associate with using AI coding tools in software development, including improvements in code quality, development efficiency, and streamlined workflows.
, an AI coding tool. This led to the next natural question about how individual developers and teams will use the time saved with AI coding tools—which motivated us to ask the question directly to our survey respondents. But first, let’s explore the benefits respondents reported in our survey.
Previous GitHub research has shown an up to 55% increase in productivity among developers who use GGitHub Copilot, , an AI coding tool.
Improved code quality
Most respondents in the U.S. (90%) and India (81%), along with more than half in Brazil (61%) and Germany (60%), reported a perceived increase in code quality when using AI coding tools.
Between 60% and 71% of survey participants reported that AI tools make it easier to learn a new programming language or understand an existing code base.
Test case generation
Overall, more than 98% of respondents reported their organizations have experimented with using AI coding tools to generate test cases. The majority of respondents reported their organizations use AI tools for test generation at least “sometimes.” That trend is most pervasive in the U.S. (reported by 92%) and the least pervasive in Germany (reported by 65%).
In the survey, respondents most commonly reported using the time they save with AI coding tools to design systems, collaborate, and learn. Specifically, 47% of respondents in the U.S. and Germany used this extra time for collaboration and system design. This continues a trend we first observed last year in a survey measuring AI’s impact on developer experience among U.S.-based developers, where respondents then reported that AI helped them focus on high-level tasks.
What are the expectations among those who have tried using AI at work?
From 61% of respondents in Germany to 73% in the US believe that AI tools improve their ability to create software that best meets customer requirements.
Security matters
99-100% of respondents expect AI tools to improve code security and developer efficiency, and between 59% in India and 67% in the US said that security teams at their companies manually review changes to the codebase.
AI tools such as Copilot Autofix in GitHub Advanced Security (GHAS) offer a promising solution to this problem by automating vulnerability identification and fixes.
Crucial insights about the evolving landscape of software development
Generative AI is rapidly transforming software development. Nearly all respondents in our survey have now tried AI coding tools, either personally or professionally (or both).
Respondents note multiple benefits when using AI coding tools. Collaboration and system design are the more strategic tasks where developers invest the time saved from using AI tools
To realize the full potential of AI, companies should focus on fostering adoption through trust, clear guidelines, and measurable outcomes.
The potential of AI-driven software development is undeniable. By prioritizing a strategic approach that balances innovation, security, and organizational alignment, we can unlock its full potential—and this is an exciting time for engineering leaders to leverage these advancements and propel their engineering teams forward.
Useful links:
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Digital Public Goods
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Digital/internet commons (EU)
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Digital technologies in Civic Tech and GovTech
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Civic Tech and Civil Society
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Public sector. Open Source Solutions
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Civic technology. Open Source Solutions
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