BenchBuilder Pipeline System

• BenchBuilder Pipeline is an automated, modular, and scalable system that constructs evaluation benchmarks from heterogeneous sources with minimal human intervention.
• It employs multi-stage architectures—including data ingestion, decomposition, test case generation, and labeling—to produce reliable and testable benchmark outputs.
• It adapts to diverse domains such as code generation, LLM evaluation, and hardware acceleration while supporting continuous updates and quality metric-driven improvements.

A BenchBuilder Pipeline is a fully automated, modular, and scalable data or workflow curation system designed to construct high-quality evaluation benchmarks from heterogeneous sources with minimal or no human intervention. The paradigm is characterized by pipelined multi-stage architectures that automate acquisition, decomposition, test case construction, problem reformatting, and labeling. BenchBuilder Pipelines have been implemented across code generation, LLM evaluation, and software-hardware acceleration domains, exemplified by systems such as CodeFlowBench, CoreCodeBench, Arena-Hard-Auto, and Courier-FPGA. These systems emphasize continuous updates, precise structural decomposition, testable and reliable outputs, and downstream suitability for rigorous model evaluation (Wang et al., 30 Apr 2025, Fu et al., 4 Jul 2025, Li et al., 17 Jun 2024, Miyajima et al., 2014).

1. Architectural Principles and Components

BenchBuilder Pipelines operate as multi-stage, end-to-end orchestrations that begin from raw data or software artifacts and culminate in benchmark entries with comprehensive metadata, decomposition, and validation assets. Architectures are modular, allowing swapping of data sources, model components, or task-specific processing logic. Standard pipeline components include:

Module	Input/Output	Process Performed
Data Ingestion	Raw data streams	Scraping, selection, normalization
Decomposition	Structured data	Task/Function extraction, dependency parse
Test Extraction	Code/tests	Unit test mapping, I/O capture, dedupe
Filtering/Labeling	Extracted tasks	Quality score, cluster, auto-label
Benchmark Output	Labeled tasks	Standardized JSON/ML-ready output

A representative example is CodeFlowBench, where the pipeline’s stages are: problem scraping, editorial scraping, code generation & verification, subproblem decomposition, test-case extraction, and structural metric labeling (Wang et al., 30 Apr 2025).

2. Pipeline Stages and Methodologies

Stage 1: Data Acquisition & Normalization

Inputs are sourced from live streams (e.g., crowdsourced queries in Arena-Hard-Auto (Li et al., 17 Jun 2024)), programming-challenge websites (Codeforces in CodeFlowBench (Wang et al., 30 Apr 2025)), or source repositories (PyPI GitHub projects in CoreCodeBench (Fu et al., 4 Jul 2025)). Data is ingested through platform APIs, web scraping, directory analysis, or dynamic tracing. Output is canonicalized, e.g., as JSON records indexed by IDs, containing all relevant problem or artifact metadata.

Stage 2: Decomposition and Structure Extraction

The pipeline then performs structural decomposition. For code benchmarks, verified solutions are parsed into Abstract Syntax Trees (ASTs), and a dependency graph $G = (V, E)$ of function calls is constructed. Each node is annotated by depth, dependencies, and contextual metadata. These dependency trees provide the basis for multi-turn codeflow benchmarking (Wang et al., 30 Apr 2025). Repository-scale pipelines (CoreCodeBench) extract function-level call graphs via dynamic execution tracing, with nodes mapped to files and AST spans (Fu et al., 4 Jul 2025).

Stage 3: Test Case Generation and Validation

Test harnesses are constructed by in-situ instrumentation (e.g., monkey-patching I/O, dynamic logging of function arguments/returns) to collect input-output (I/O) pairs for both top-level problems and subproblems. For existing repositories, mapping source functions to associated tests is performed via heuristics, LLM prompts, and directory analysis. Auto-generated or re-used test cases are deduplicated and sorted, and unreliable cases are filtered out if their masked versions pass $>10\%$ of tests (Fu et al., 4 Jul 2025).

Stage 4: Filtering, Quality Scoring, and Labeling

BenchBuilder Pipelines incorporate automated quality control. For open-ended prompt benchmarks (Arena-Hard-Auto), LLM annotators score prompts for qualities such as specificity and technical accuracy, and low-quality clusters are excluded (Li et al., 17 Jun 2024). For code, only solutions passing reference output validation are retained; unreliable or trivial problem variants are filtered via programmatic and LLM-based checks. Structural metrics (overall turns, depth, dependency complexity) and information gain labels are computed for each benchmark entry (Wang et al., 30 Apr 2025, Fu et al., 4 Jul 2025).

Stage 5: Benchmark Assembly and Output

The end-product is a fully annotated benchmark entry including:

• Comprehensive metadata (ID, description, tags, limits)
• Structural decomposition (dependency trees or call graphs)
• For each subproblem or task:
  • Natural-language description
  • Function signature and context attributes
  • Reference implementation and input-output test suite
• Structural and reliability metrics
• Structured output (e.g., JSONL or database) suitable for downstream automated evaluation

Continuous update mechanisms are integral: pipelines are scripted to poll new data (daily or weekly), regenerate decompositions, and re-publish refreshed benchmark sets (Wang et al., 30 Apr 2025, Li et al., 17 Jun 2024).

3. Automation, LLM Roles, and Verification Protocols

LLMs are leveraged throughout BenchBuilder Pipelines as annotators, decomposers, and judges:

• Prompt extraction and clustering (via embeddings and HDBSCAN/UMAP)
• Quality scoring (LLM review for key criteria per instance)
• Subproblem decomposition (LLM synthesis of problem statements from code fragments, eg. Deepseek-V3 in CodeFlowBench (Wang et al., 30 Apr 2025))
• Challenge construction (via masking, bug introduction, TDD scenario synthesis (Fu et al., 4 Jul 2025))
• Model evaluation (“LLM-as-a-Judge” pairwise preference assessments (Li et al., 17 Jun 2024))

Rigorous verification is enforced using platform-native test judges (e.g., the Codeforces judge), formal statistical checks (bootstrap CIs for model separability), and information gain filters to retain only discriminative problems (Wang et al., 30 Apr 2025, Li et al., 17 Jun 2024, Fu et al., 4 Jul 2025).

4. Design Generalization and Configurability

BenchBuilder Pipelines are designed for extensibility. They are language-agnostic where decomposition, dependency extraction, and test capture can be adapted to any language with AST and test framework support. By replacing front-end data acquisition and plugging in alternative judges, the pipeline templates generalize to new domains (e.g., LeetCode, AtCoder, Java codebases) (Wang et al., 30 Apr 2025, Fu et al., 4 Jul 2025). Repository-level pipelines expose hyperparameters (max call-tree depth, number of functions, problem types) for granular control over benchmark scenario construction and difficulty (Fu et al., 4 Jul 2025).

Template configurations are defined in JSON or Python dicts, and pipeline build APIs allow automation from any compatible data source.

5. Applications and Impact in Research

BenchBuilder Pipelines underpin the creation of robust, challenging, and continuously fresh benchmarks, supporting:

• Evaluation of LLM capabilities in code generation, repair, reasoning, and planning (CodeFlowBench (Wang et al., 30 Apr 2025), CoreCodeBench (Fu et al., 4 Jul 2025))
• Distinction and ranking of state-of-the-art models under standardized metrics, with high correlation to human preferences and statistically tight separability (Li et al., 17 Jun 2024)
• Systematic tracking of advances: for instance, models show significant degradation in pass rates as multi-turn and dependency complexity increases, revealing frontiers for LLM research (Wang et al., 30 Apr 2025, Fu et al., 4 Jul 2025)
• Scalable expansion of benchmarks for new scenarios or domains (automation cost: $<$\$25 for 500 prompts at LLM API rates (Li et al., 17 Jun 2024))

6. Extending to Hybrid and HW/SW Workflows

Courier-FPGA demonstrates that BenchBuilder design principles can be extended beyond LLM and code evaluation to hybrid software/hardware acceleration. The pipeline comprises dynamic trace collection, call graph construction, SW/HW mapping under resource constraints, TBB-based parallel pipeline generation, dynamic offloading, and statistical throughput modeling. Generalization strategies abstract front-end instrumentation, extend to arbitrary kernels, rely on pluggable code generators, and use performance libraries to tune pipeline mapping (Miyajima et al., 2014). This suggests a plausible implication: the BenchBuilder paradigm can unify benchmark generation and hardware-software pipeline optimization under a single architectural framework.

7. Limitations, Challenges, and Future Directions

BenchBuilder Pipelines depend on the quality of upstream LLMs for annotation and decomposition, yielding potential biases or inaccuracies. Output reliability is tied to the quality of source data (editorials, user queries), the robustness of filtering heuristics, and test extraction coverage (Li et al., 17 Jun 2024, Fu et al., 4 Jul 2025). Mitigation includes ensemble judging, expansion of filtering criteria, static analysis or symbolic validation adjuncts, and addition of new languages and domains. The architecture’s modularity and automation afford continuous improvement, scalable updates, and rapid response to evolving model evaluation needs.