Multi-Docker-Eval Benchmark

• Multi-Docker-Eval Benchmark is an evaluation suite that automates the creation of containerized execution environments from complex software repositories.
• The benchmark employs Docker orchestration to measure real-world reproducibility, dependency management, and efficiency using metrics such as the fail-to-pass rate and commit rate.
• Experimental protocols reveal that multi-agent frameworks significantly improve environment repair success, guiding future improvements in automated container orchestration.

Multi-Docker-Eval benchmarks are specialized evaluation suites that rigorously quantify the effectiveness and efficiency of automated environment setup and containerized execution in software engineering and computational research contexts. By leveraging container orchestration—most commonly with Docker—these benchmarks target real-world reproducibility, dependency management, and performance assessment across diverse codebases and domains. The design principles, dataset construction, metrics, and trends observed in recent research define the current state of Multi-Docker-Eval benchmarking and its broader impact on automation pipelines and experimental reproducibility.

1. Benchmark Definition and Dataset Construction

Multi-Docker-Eval is premised on challenging the automated construction of execution environments for real software repositories extracted from public ecosystems. The prototypical instance, as defined in "Multi-Docker-Eval: A `Shovel of the Gold Rush' Benchmark on Automatic Environment Building for Software Engineering" (Fu et al., 7 Dec 2025), comprises 40 open-source repositories distributed across nine prominent programming languages: Python, C, C++, Go, Java, JavaScript, PHP, Rust, and Ruby. Selection criteria on GitHub repositories ensure nontrivial complexity:

• 1,000–1,500 stars
• ≥ 20 forks
• ≥ 10 contributors
• ≤ 100 MB repository size

For each repository, up to eight post-July 2025 pull requests are sampled to construct 334 labeled (issue, patch) pairs. Difficulty annotation divides instances as "Easy" or "Hard," depending on the feasibility of producing a testable Docker image with canonical instructions. Empirically, only 20.06 % are "Easy," confirming barrier complexity at scale.

2. Formal Task Specification

The Multi-Docker-Eval task is a mapping from the tuple $\langle R, D, P^* \rangle$ to two deliverables:

• A test function $T(\cdot)$ with $T(R) = \mathrm{fail}$ and $T(R \oplus P^*) = \mathrm{pass}$
• A Docker environment $E$ (Dockerfile plus auxiliary scripts) such that $T$ executes

Agents receive the raw source code $R$, a natural language issue description $D$, and the canonical patch $P^*$. Successful completion demands that both the container build ($\mathrm{docker\ build}(E)$) and subsequent test execution pass rigorous semantic checks in bounded resource and time domains:

• Docker build timeout: 1,800 s
• Test execution timeout: 2,700 s

Satisfactorily built environments must reproduce the reported defect and confirm remediation under the patch.

3. Evaluation Metrics and Statistical Frameworks

Two complementary axes dominate measurement paradigms: outcome quality and process efficiency. Primary metrics include:

3.1 Outcome Metrics

• Fail-to-Pass Rate (F2P):

$\mathrm{F2P} = \frac{TP}{N} \times 100\%$

where $TP$ is the number of instances with transition from failing to passing state after patch application; $N$ is the benchmark size.

• Commit Rate:

$$\mathrm{CommitRate} = \frac{|\{i : \mathrm{agent\_submitted}_i\}|}{N} \times 100\%$$

3.2 Process Metrics

• Token consumption (prompt + completion)
• Wall time (seconds)
• CPU time (aggregate)
• Max RSS (GB)
• Average Docker image size (GB)

Empirical variance is tracked via repeated executions to distinguish agent instability from infrastructure variation. In database-oriented Multi-Docker benchmarks (cf. (Grambow et al., 2018)), latency averages ($\mu$), standard deviations ($\sigma$), and variability indices ($V = \sigma / \mu$) are standard, with confidence intervals computed as $CI = 1.96 \cdot (\sigma / \sqrt{n})$.

4. Experimental Protocols and Representative Frameworks

Recent experiments implement containerized isolation on multi-core servers. In (Fu et al., 7 Dec 2025), runs are orchestrated without GPU, on 32-core Xeon hosts (128 GB RAM, 1 TB disk), and allow up to 16 parallel builds. Agent frameworks compared include:

• SWE-Builder (multi-agent): Four subagents specialize and share validated environment memory pools, supporting iterative repair.
• RepoLaunch (single-agent): Direct bash scripting in base images with no memory reuse or retry logic.

Ten LLM configurations are benchmarked (DeepSeek-v3.1, DeepSeek-R1, Qwen3-235B-A22B, GPT-OSS-20B, GPT-OSS-120B, Kimi-K2-0905, Kimi-K2-thinking, Claude-Sonnet-4, GPT-5-Mini, Gemini-2.5-Flash), with each task repeated thrice to suppress stochastic flakiness.

For database performance, (Grambow et al., 2018) introduced four dockerization variants, comparing native and dockerized setups for clients and system-under-test (SUT) with automated multi-replicate orchestration, parameter sweeps, and system metric collection.

5. Key Empirical Findings and Failure Modes

Evaluation on (Fu et al., 7 Dec 2025) substantiates core bottlenecks and trends:

• Success rates: F2P peaks at 37.72 % (DeepSeek-v3.1 with SWE-Builder), with most LLM configurations <38 %. The environment construction (dependency resolution, build system quirks) dominates failure rates (36.1 %).
• Framework efficacy: Multi-agent, feedback-driven (SWE-Builder) frameworks exceed single-agent by 2.5×–3.5× in F2P.
• Process efficiency: Model size and reasoning length do not guarantee improved F2P. Token usage, wall time, and image size are critical for scaling workflows.
• Language heterogeneity: Go yields F2P=54.5 %, exploiting standardized build/test pipelines; Python and JavaScript follow, while C/C++, Java, and Rust trail due to brittle dependency scenarios.
• Failure categorization: Apart from Docker build errors, test-script generation and test harnesses that yield "silent false positives" constitute another 30 % of all failures.
• Comparison with bare-metal: Containerization imposes non-linear, non-constant overheads (CPU: <3 %, I/O: up to 5 %, network: up to 4 %) (Grambow et al., 2018), which vary with workload saturation and infrastructure.

6. Design Guidelines, Reproducibility Strategies, and Calibration Models

Actionable recommendations from (Fu et al., 7 Dec 2025) and (Grambow et al., 2018) for next-generation Multi-Docker-Eval design include:

1. Multi-agent iterative workflows: Feedback loops and validated environment memory pools mitigate dependency and test generation brittleness.
2. System-level dependency reasoning: LLMs require declarative, grounded knowledge of OS- and toolchain-level packages.
3. Language and ecosystem standardization: Benchmarks should exploit declarative build/test pipelines for higher reliability.
4. Use of open-source models: Empirical results support the adoption of lightweight, community-driven LLMs for better performance-to-cost ratios.
5. Resource-constrained evaluation: Realistic timeouts, token limits, and memory caps are critical for publicly deployable automation.
6. Calibration and variance control: Use empirical linear models for Docker overheads (e.g., $$Od(U,T) \approx \alpha + \beta U + \gamma T^{-\delta}$$), replicate each trial $\geq5$ times, randomize order of runs, and apply statistical analyses (CI, t-tests, MDE).

Quantitative correction and comparison between dockerization setups require formulas such as $L_{\text{corr}} = L_{\text{docker}} / (1 + Od)$ and cross-container calibration equations.

7. Implications, Limitations, and Future Directions

Multi-Docker-Eval benchmarks supply robust, reproducible frameworks for quantifying the real-world effectiveness of environment automation and container-based workflows in software engineering and scientific computing. Their use delineates several important boundaries:

• Direct Docker-induced overheads are modest but workload-dependent and non-constant.
• Relative performance should be assessed within the same containerization regime; Docker-to-native comparisons must be carefully calibrated.
• Fully automated pipelines remain unsolved for general software; F2P and process efficiency limits suggest need for further algorithmic development, agent feedback mechanisms, and improved dependency reasoning.
• Benchmark orchestration must embed statistical analysis, automated provisioning, and adaptive repetition to ensure precision and fidelity.

A plausible implication is that embracing ecosystem-specific conventions, multi-agent repair strategies, and resource-aware design is necessary for scalable and robust automation pipelines. Benchmarks must incorporate rigorous statistical guardrails and calibration models to yield scientifically valid, reproducible results in multi-container settings (Fu et al., 7 Dec 2025, Grambow et al., 2018, Arango et al., 2017, Papenmeier et al., 27 May 2025).