The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers

TL;DR

This paper introduces RealHumanEval, a human-centric evaluation platform for large language models assisting programmers, revealing that benchmark improvements do not fully translate to real-world productivity gains and highlighting the need for better evaluation proxies.

Contribution

The paper presents RealHumanEval, a new web-based tool for assessing LLMs in real programming tasks, and provides insights into the gap between benchmark performance and actual programmer productivity.

Findings

Benchmark improvements correlate with increased productivity but not proportionally.

Programmer preferences do not align with actual performance.

Gaps between benchmark and human performance persist across support types.

Abstract

Evaluation of large language models for code has primarily relied on static benchmarks, including HumanEval (Chen et al., 2021), or more recently using human preferences of LLM responses. As LLMs are increasingly used as programmer assistants, we study whether gains on existing benchmarks or more preferred LLM responses translate to programmer productivity when coding with LLMs, including time spent coding. We introduce RealHumanEval, a web interface to measure the ability of LLMs to assist programmers, through either autocomplete or chat support. We conducted a user study (N=243) using RealHumanEval in which users interacted with seven LLMs of varying base model performance. Despite static benchmarks not incorporating humans-in-the-loop, we find that improvements in benchmark performance lead to increased programmer productivity; however gaps in benchmark versus human performance are not proportional -- a trend that holds across both forms of LLM support. In contrast, we find that programmer preferences do not correlate with their actual performance, motivating the need for better proxy signals. We open-source RealHumanEval to enable human-centric evaluation of new models and the study data to facilitate efforts to improve code models.