ContextBench: A Benchmark for Context Retrieval in Coding Agents

TL;DR

ContextBench is a comprehensive benchmark designed to evaluate how effectively coding agents retrieve and utilize code context during problem solving, revealing gaps and guiding improvements in LLM-based coding tools.

Contribution

It introduces a process-oriented evaluation framework with gold-context metrics, covering multiple languages and agent types, to analyze context retrieval in coding agents.

Findings

LLMs favor recall over precision in context retrieval

Sophisticated scaffolding yields marginal improvements

Significant gaps between explored and utilized context exist

Abstract

LLM-based coding agents have shown strong performance on automated issue resolution benchmarks, yet existing evaluations largely focus on final task success, providing limited insight into how agents retrieve and use code context during problem solving. We introduce ContextBench, a process-oriented evaluation of context retrieval in coding agents. ContextBench consists of 1,136 issue-resolution tasks from 66 repositories across eight programming languages, each augmented with human-annotated gold contexts. We further implement an automated evaluation framework that tracks agent trajectories and measures context recall, precision, and efficiency throughout issue resolution. Using ContextBench, we evaluate four frontier LLMs and five coding agents. Our results show that sophisticated agent scaffolding yields only marginal gains in context retrieval ("The Bitter Lesson" of coding agents), LLMs consistently favor recall over precision, and substantial gaps exist between explored and utilized context. ContextBench augments existing end-to-end benchmarks with intermediate gold-context metrics that unbox the issue-resolution process. These contexts offer valuable intermediate signals for guiding LLM reasoning in software tasks.