Join top executives in San Francisco on July 11-12 to learn how leaders are integrating and optimizing AI investments for success. A recent survey conducted by GitHub in partnership with Wakefield Research reveals the impact of AI on the developer experience. The survey involved 500 U.S.-based developers from companies with 1,000-plus employees and focused on key aspects of their careers. The findings show that 92% of developers already use AI-powered coding tools in their work. Despite investments in DevOps, developers still face challenges, with their most time-consuming task being waiting on builds and tests. They also express concerns about repetitive tasks and aspire to have more time for collaboration, acquiring new skills, and creating innovative solutions. According to GitHub, these statistics indicate a growing need for improving efficiency in the development process. Join the event in San Francisco on July 11-12 to hear from top executives who have successfully integrated and optimized AI investments and learn how to avoid common pitfalls. Register now.
In a statement to VentureBeat, Inbal Shani, the Chief Product Officer at GitHub, said that developers spend most of their time writing code and tests and then waiting for the code to be reviewed or for builds to finish. However, AI-powered coding tools can enhance individual developer productivity and team collaboration, leading to greater impact, increased satisfaction, and more innovative solutions. Collaboration was found to be a vital aspect of the developer experience, with over 80% of developers believing that AI-powered coding tools can improve team collaboration, code quality, project completion speed, and incident resolution. Shani emphasized that collaboration is a force multiplier for engineering teams to drive customer results and that organizations should prioritize developers by identifying areas of friction and eliminating productivity barriers.
The survey also revealed that developers value upskilling and productivity gains as the top benefits of AI tools. Integrating AI-powered coding tools into their workflow provides them with an opportunity to improve performance and meet existing standards. Code quality and collaboration were identified as the most important performance metrics by developers, who believe that they should be evaluated based on these criteria. Effective collaboration was found to improve code quality, and developers highlighted regular touchpoints, uninterrupted work time, access to fully configured developer environments, and mentor-mentee relationships as critical factors for successful collaboration. Developers want organizations to incentivize collaboration as a top performance metric and invest in cost-effective measures like mentorship programs to facilitate growth and upskilling.
GitHub suggests that organizations invest in enterprise-grade AI coding tools that align with efficacy and data privacy criteria and assist developers in integrating and optimizing their workflows around these tools. Cultural changes and proactive change management are necessary for the successful deployment of AI coding tools. Organizations should establish workplace programs and policies that promote collaboration, schedule regular check-ins and meetings, and provide platforms for asynchronous communication. Standardizing developer environments through cloud-based IDEs or alternative solutions can minimize time spent on machine setup and enable developers to focus on collaborative problem-solving. Mentor-mentee relationships should also be encouraged to facilitate the growth and skills development of junior developers.
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