RealHumanEval: A Web Interface to Measure the Ability of LLMs to Assist Programmers

Evaluating the Real Impact of AI on Programmer Productivity **Understanding the Problem** As large language models (LLMs) are increasingly used in coding, we need to measure their real impact on programmer productivity. Current methods only check if the code is correct and ignore how LLMs work with humans during actual coding tasks. **Why a New Evaluation Method is Needed** Many LLMs help with programming, but their effectiveness is often measured using outdated methods. These methods do not reflect how programmers actually use LLMs. Important aspects like coding time, acceptance of suggestions, and problem-solving help are often overlooked. This raises doubts about the usefulness of traditional evaluation methods. **Introducing RealHumanEval** Researchers from top institutions developed **RealHumanEval**, a new platform to assess LLMs by focusing on human interactions. It evaluates LLMs in real-time through two modes: autocomplete suggestions and chat-based assistance. The platform tracks important metrics like task completion time and how often suggestions are accepted, giving a clearer view of how LLMs impact coding in the real world. **How RealHumanEval Works** RealHumanEval tested seven different LLMs on 17 tasks of varying difficulty. It recorded details such as time spent and tasks completed during tests with 243 participants. This thorough analysis helps show how LLMs can improve efficiency in coding tasks. **Insights Gained** The testing showed that high-performing models like GPT-3.5 and CodeLlama-34b helped programmers finish tasks faster—by 19% and 15%, respectively. However, not all models were equally effective; for example, CodeLlama-7b showed less convincing evidence of improving productivity. While LLMs could speed up task completion, they didn’t significantly increase the number of tasks finished in a set time. **Conclusion: A New Standard for Evaluation** **RealHumanEval** is a significant advancement because it focuses on human-centered metrics instead of traditional benchmarks. It offers valuable insights into how LLMs assist real programmers, highlighting both their strengths and weaknesses in coding situations. **Get Involved** For more insights from this research, follow us on social media platforms like Twitter, Telegram, and LinkedIn. Join our community and subscribe to our newsletter for updates. **Transform Your Business with AI Solutions** To stay competitive and use AI effectively, explore how **RealHumanEval** can benefit your team. Identify areas where AI can automate processes, set key performance indicators (KPIs), select appropriate AI tools, and implement solutions step by step. For assistance with AI strategy, contact us at hello@itinai.com and stay updated with our insights on Telegram and Twitter. Discover how AI can enhance your operations at itinai.com.