TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation

TL;DR

TestExplora introduces a benchmark for evaluating large language models as proactive software testers, revealing current models' limited bug discovery capabilities and emphasizing the importance of agentic exploration for autonomous quality assurance.

Contribution

We present TestExplora, a novel benchmark with realistic, repository-level tasks that evaluates LLMs' ability for proactive bug discovery using documentation as an oracle.

Findings

State-of-the-art models achieve a maximum Fail-to-Pass rate of 16.06%.

Agentic exploration with GPT-5-mini improves F2P to 17.27% and F2P@5 to 29.7%.

Navigating cross-module interactions is key to enhancing bug discovery.

Abstract

Given that Large Language Models (LLMs) are increasingly applied to automate software development, comprehensive software assurance spans three distinct goals: regression prevention, reactive reproduction, and proactive discovery. Current evaluations systematically overlook the third goal. Specifically, they either treat existing code as ground truth (a compliance trap) for regression prevention, or depend on post-failure artifacts (e.g., issue reports) for bug reproduction-so they rarely surface defects before failures. To bridge this gap, we present TestExplora, a benchmark designed to evaluate LLMs as proactive testers within full-scale, realistic repository environments. TestExplora contains 2,389 tasks from 482 repositories and hides all defect-related signals. Models must proactively find bugs by comparing implementations against documentation-derived intent, using documentation as the oracle. Furthermore, to keep evaluation sustainable and reduce leakage, we propose continuous, time-aware data collection. Our evaluation reveals a significant capability gap: state-of-the-art models achieve a maximum Fail-to-Pass (F2P) rate of only 16.06%. Further analysis indicates that navigating complex cross-module interactions and leveraging agentic exploration are critical to advancing LLMs toward autonomous software quality assurance. Consistent with this, SWEAgent instantiated with GPT-5-mini achieves an F2P of 17.27% and an F2P@5 of 29.7%, highlighting the effectiveness and promise of agentic exploration in proactive bug discovery tasks.