GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging - Ni, Ziyi; Wang, Huacan; Zhang, Shuo; Lu, Shuo; He, Ziyang, WangYou; Tang, Zhenheng; Hu, Sen; Li, Bo; Hu, Chen; et al. (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Beyond scratch coding, exploiting large-scale code repositories (e.g., GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evaluate code agents in such authentic, workflow-driven scenarios. To bridge this gap, we introduce GitTaskBench, a benchmark designed to systematically assess this capability via 54 realistic tasks across 7 modalities and 7 domains. Each task pairs a relevant repository with an automated, human-curated evaluation harness specifying practical success criteria. Beyond measuring execution and task success, we also propose the alpha-value metric to quantify the economic benefit of agent performance, which integrates task success rates, token cost, and average developer salaries. Experiments across three state-of-the-art agent frameworks with multiple advanced LLMs show that leveraging code repositories for complex task solving remains challenging: even the best-performing system, OpenHands+Claude 3.7, solves only 48.15% of tasks. Error analysis attributes over half of failures to seemingly mundane yet critical steps like environment setup and dependency resolution, highlighting the need for more robust workflow management and increased timeout preparedness. By releasing GitTaskBench, we aim to drive progress and attention toward repository-aware code reasoning, execution, and deployment—moving agents closer to solving complex, end-to-end real-world tasks.