GitTaskBench: Real-World Code Agent Benchmark

• GitTaskBench is a benchmark that evaluates autonomous software agents using authentic GitHub repositories to test planning, environment management, and multi-modal code comprehension.
• It encompasses 54 tasks across seven input/output modalities and functional domains, providing concrete success metrics such as CIEDE2000, SSIM, and F₁ scores.
• The benchmark integrates economic analysis through an alpha-value metric, linking technical performance with cost-effectiveness compared to human labor.

GitTaskBench is a code agent benchmark designed to rigorously evaluate the ability of autonomous software agents to solve practical tasks by leveraging large-scale code repositories. Distinguished from synthetic coding datasets, GitTaskBench measures agents’ real-world proficiency in planning, environment management, and code comprehension across diverse modalities, domains, and workflow-driven settings using authentic codebases paired with automated, human-curated evaluation harnesses (Ni et al., 26 Aug 2025).

1. Motivation and Context

Contemporary repository-centric software development relies extensively on adapting and integrating open-source codebases for varied tasks beyond de novo algorithmic coding. While prior benchmarks such as SWE-bench emphasized patch generation for issue resolution, they did not systematically expose agents to the compounded workflow, comprehension, and provisioning challenges inherent in realistic scenarios. GitTaskBench addresses this methodological shortfall by providing a curated set of tasks that demand not only code generation but also robust repository exploration, environment configuration, and multi-step execution reflective of typical developer workflows (Ni et al., 26 Aug 2025).

2. Benchmark Structure, Modalities, and Domains

GitTaskBench consists of 54 tasks selected to represent seven core input/output modalities—images, video, audio, text, physiological signals, web data, and office documents—and seven functional domains: Image Processing, Video Processing, Speech Processing, Physiological Signal Processing, Security/Privacy, Web Scraping, and Office Document Processing.

Each task presents the agent with:

• A paired, full-scale, public GitHub repository
• Clearly defined user intent and success criteria appropriate to the modality/domain (e.g., style transfer, document parsing, signal analysis)
• An evaluation harness: automated scripts specifying concrete process-related (execution success) and result-related (output quality metrics such as CIEDE2000, SSIM, or F₁ scores) criteria

This pairing ensures that the agent must understand the repository’s structure, dependencies, and APIs, then execute a (possibly multi-turn) workflow yielding outputs that meet real-world standards.

Modality	Representative Domains	Example Success Metrics
Images	Image Processing	CIEDE2000, SSIM
Video	Video Processing	SSIM, output format correctness
Audio	Speech Processing	Model accuracy, output fidelity
Text	Web Scraping, Office Doc Processing	F₁ score, semantic alignment
Signals	Physiological Signal Processing	Statistical coverage, error rates

Table: Task modalities, domains, and example evaluation metrics as specified in GitTaskBench.

3. Evaluation Metrics and Economic Analysis

GitTaskBench utilizes several key metrics:

• Execution Completion Rate (ECR): Proportion of tasks completed without errors (process status)
• Task Pass Rate (TPR): Fraction of tasks where the agent output satisfies all automated success criteria (result status)
• Alpha-value ($\alpha$): An economic benefit metric, integrating completion, output quality, market value, and agent cost. Specifically:

$$\alpha = \frac{1}{n} \sum_{i=1}^n \left[ T_i \times MV_i \times Q_i - C_i \right]$$

where $T_i$ is binary task success, $MV_i$ is market value (typical human wage or freelance fee), $Q_i$ is quality (human-rated, $[0, 1]$), and $C_i$ is agent cost (token/API expenditure).

This quantifies not only technical task accomplishment but also cost-effectiveness versus human labor—a direct estimate of practical automation value.

4. Experimental Findings and Error Analysis

Recent experiments on GitTaskBench using state-of-the-art agent frameworks and advanced LLMs yielded the following results (Ni et al., 26 Aug 2025):

• Top system (OpenHands+Claude 3.7) achieved an ECR of 72.22% and TPR of 48.15%.
• Other combinations (e.g., SWE-Agent, Aider, GPT-4 variants, DeepSeek-V3) lagged further behind.
• Performance profiles varied significantly with model choice, agent framework, and task modality.

Error analysis classifies failures into five major categories:

• E1: Environment Setup (65% of failures, e.g., version conflicts, missing dependencies)
• E2: Workflow Planning (misuse or misunderstanding of repository documentation)
• E3: Repository Comprehension (inaccurate identification of entry-point or API usage)
• E4: Runtime Execution (unhandled exceptions, incomplete runs)
• E5: Instruction Non-compliance (failure to follow precise task instructions)

This suggests the principal bottleneck lies in mundane but critical steps, such as dependency resolution and build environment preparation, rather than in core code generation logic.

5. Methodological Innovations and Analysis

Essential methodological attributes of GitTaskBench:

• Repository pairing: All tasks require real-world repository usage, elevating the challenge relative to benchmarks focused on isolated code snippets.
• Automated human-curated evaluation harnesses: These enable objective “process” and “result” status checks, ensuring reproducibility without manual judgment.
• Economic performance analysis: By linking technical and cost metrics, the benchmark informs on the broader economic implications of agent usage.

The comprehensive task diversity (modality, domain, repository size) exposes agents to environment setup, documentation parsing, workflow orchestration, and code adaptation, providing a multi-faceted assessment.

6. Access, Resources, and Open Research Directions

GitTaskBench is fully open-sourced; both tasks and evaluation scripts are available at https://github.com/QuantaAlpha/GitTaskBench. The repository contains:

• Task definitions and user intent specifications
• Corresponding evaluation harnesses
• Prompt templates and reproducibility guidelines
• Automated logs for transparent error/failure tracking

Recommended future research directions include:

• Enhanced workflow management, notably adaptive timeout regimes and iterative environment setup
• Improved error-handling mechanisms for dependency resolution
• Algorithmic advances in documentation parsing, codebase mapping, and API extraction
• Exploration of scaling to additional domains, modalities, and workflow scenarios

7. Significance and Impact

GitTaskBench represents a transition from algorithmic code generation toward holistic, workflow-driven software agent assessment. By revealing practical, infrastructural bottlenecks (especially around environment provisioning and repository exploitation), it directs attention to the structural aspects of real-world development. The economic metric ($\alpha$) further grounds research in automation’s tangible value proposition.

Recent agent frameworks such as RepoMaster have demonstrated significant performance boosts on GitTaskBench by combining hierarchical code analysis, context-efficient repository summarization, and targeted exploration (Wang et al., 27 May 2025), but current top performance remains below 50% complete task pass rate—underscoring the challenge and ongoing opportunity in this domain.

GitTaskBench is thus positioned to catalyze advances not only in autonomous code generation but also in agentic workflow management, environment reasoning, and repository-centric development, fostering systematic progress in AI-driven software engineering.