{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"big-code-bench","slug":"big-code-bench","name":"Big Code Bench","type":"benchmark","url":"https://github.com/bigcode-project/bigcodebench","page_url":"https://unfragile.ai/big-code-bench","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"big-code-bench__cap_0","uri":"capability://code.generation.editing.multi.split.code.generation.task.evaluation.with.pass.k.metrics","name":"multi-split code generation task evaluation with pass@k metrics","description":"Evaluates LLM code generation across 1,140 realistic programming tasks organized into two splits (Complete for all models, Instruct for chat models) using pass@k statistical metrics that measure the probability at least one of k generated samples passes all test cases. The system generates multiple code samples per task, executes each against embedded test suites, and aggregates results into pass@1, pass@10, pass@100 metrics for comparative model analysis.","intents":["Compare code generation capabilities across different LLM models using standardized metrics","Evaluate how well models handle library-specific programming (NumPy, Pandas, Matplotlib)","Measure improvement in code generation quality over model iterations","Identify which model architectures excel at practical programming vs toy problems"],"best_for":["ML researchers benchmarking code generation models","LLM teams evaluating model releases against industry standards","Organizations selecting between commercial and open-source code models"],"limitations":["Pass@k metrics require generating k samples per task, creating computational overhead (1,140 tasks × k samples)","Test case coverage may not capture all edge cases or production-grade code quality concerns","Metrics assume deterministic test execution; flaky tests or environment-dependent code may produce inconsistent results","Does not measure code readability, maintainability, or adherence to style conventions"],"requires":["Python 3.9+","LLM API access (OpenAI, Anthropic, Ollama, or local model)","Execution environment (local, E2B sandbox, or Hugging Face Gradio)","Sufficient compute for generating multiple samples per task"],"input_types":["task prompts (natural language instructions or docstrings)","model identifiers and configuration","temperature and sampling parameters"],"output_types":["pass@k metrics (JSON with pass@1, pass@10, pass@100 scores)","per-task evaluation results with pass/fail status","leaderboard-formatted comparison data"],"categories":["code-generation-editing","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_1","uri":"capability://tool.use.integration.unified.multi.provider.code.generation.with.model.abstraction.layer","name":"unified multi-provider code generation with model abstraction layer","description":"Provides a unified interface for generating code samples across heterogeneous LLM providers (OpenAI, Anthropic, Ollama, local models) through a provider-agnostic abstraction that handles API differences, authentication, and response parsing. The system maps provider-specific APIs to a common code generation interface, enabling seamless model swapping without changing benchmark code.","intents":["Generate code samples from multiple LLM providers using identical prompts for fair comparison","Switch between cloud and local models without modifying evaluation scripts","Support both proprietary (GPT-4, Claude) and open-source (Ollama) models in the same benchmark run","Isolate provider-specific implementation details from benchmark logic"],"best_for":["Researchers comparing across model families (OpenAI vs Anthropic vs open-source)","Teams running benchmarks in hybrid environments (cloud + local models)","Organizations avoiding vendor lock-in by supporting multiple providers"],"limitations":["Provider abstraction adds latency overhead for request marshaling and response normalization","Rate limiting and quota management must be handled per-provider, complicating large-scale runs","Some advanced features (e.g., vision capabilities, tool use) may not be uniformly supported across providers","API key management and authentication complexity increases with each new provider"],"requires":["API keys for desired providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","Provider-specific Python SDKs installed","Network access to provider endpoints or local Ollama server running"],"input_types":["provider name (openai, anthropic, ollama, local)","model identifier (gpt-4, claude-3-opus, etc.)","prompt text and generation parameters"],"output_types":["generated code strings","provider-normalized metadata (tokens used, finish reason)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_10","uri":"capability://code.generation.editing.model.configuration.and.generation.parameter.tuning","name":"model configuration and generation parameter tuning","description":"Supports configurable generation parameters (temperature, top_p, max_tokens, n_samples) that control LLM sampling behavior and output diversity. Users can specify different parameter sets per model, enabling exploration of temperature-quality tradeoffs and sample efficiency without code changes.","intents":["Tune generation parameters to optimize pass@k performance for specific models","Explore temperature-quality tradeoffs (deterministic vs diverse sampling)","Control sample budget per task (n_samples) to balance cost vs coverage","Compare model performance across different generation configurations"],"best_for":["Researchers optimizing model sampling strategies","Teams tuning generation parameters for production deployments","Organizations exploring cost-quality tradeoffs in code generation"],"limitations":["Parameter tuning requires multiple benchmark runs, increasing total compute cost","Optimal parameters may be task-dependent; global tuning may not generalize","Parameter sensitivity varies across models; no universal optimal configuration","No built-in hyperparameter search; users must manually specify parameter grids"],"requires":["Model-specific parameter ranges (temperature, top_p, max_tokens)","Configuration file or CLI arguments specifying parameters","Sufficient compute budget for multiple benchmark runs"],"input_types":["temperature (0.0-2.0)","top_p (0.0-1.0)","max_tokens (integer)","n_samples (integer, typically 1-100)"],"output_types":["pass@k metrics for each parameter configuration","comparison data showing parameter sensitivity"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_2","uri":"capability://automation.workflow.sandboxed.code.execution.with.multiple.environment.backends","name":"sandboxed code execution with multiple environment backends","description":"Executes generated code samples in isolated environments using pluggable backends (local execution with safety limits, E2B sandbox for remote execution, Hugging Face Gradio spaces) that prevent malicious or buggy code from affecting the host system. Each backend enforces resource limits, timeout constraints, and dependency isolation while capturing stdout/stderr and execution results for evaluation.","intents":["Safely execute untrusted code generated by LLMs without risking host system compromise","Run code in isolated environments with controlled dependencies and library versions","Measure code execution time and resource consumption across different execution backends","Support distributed evaluation by offloading execution to remote sandboxes"],"best_for":["Researchers evaluating code generation from untrusted models","Teams running benchmarks on shared infrastructure requiring strong isolation","Organizations needing remote execution for scalability (E2B, Gradio backends)"],"limitations":["Local execution backend provides limited isolation; requires careful resource limits to prevent DoS","E2B and Gradio backends introduce network latency and dependency on external services","Timeout enforcement may prematurely terminate legitimate long-running code","Environment-specific behavior (OS differences, library versions) may cause inconsistent results across backends","Docker-based sandboxing adds ~500ms-1s overhead per execution compared to local execution"],"requires":["Python 3.9+ for local backend","E2B API key and account for E2B backend","Docker installation for E2B sandbox templates","Network access to Hugging Face for Gradio backend","Timeout and memory limits configured per environment"],"input_types":["generated Python code string","test cases and expected outputs","execution environment configuration (timeout, memory limits)"],"output_types":["execution result (pass/fail)","stdout/stderr output","execution time and resource metrics","error traces for failed executions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_3","uri":"capability://safety.moderation.code.sanitization.and.syntax.validation.before.execution","name":"code sanitization and syntax validation before execution","description":"Pre-processes generated code through a sanitization pipeline that removes unsafe patterns (e.g., file system operations, network calls) and validates Python syntax using AST parsing before execution. The system identifies and flags code that violates safety constraints, preventing execution of malicious or structurally invalid code while maintaining semantic correctness for legitimate implementations.","intents":["Prevent execution of code containing dangerous operations (file I/O, subprocess calls, network requests)","Catch syntax errors early before expensive sandbox execution","Identify code generation failures (incomplete functions, invalid Python) without running them","Enforce consistent code safety policies across all evaluated models"],"best_for":["Benchmarks evaluating untrusted model outputs","Teams needing deterministic safety checks before execution","Researchers analyzing code generation failure modes (syntax errors, unsafe patterns)"],"limitations":["Sanitization rules may be overly conservative, rejecting legitimate code that uses file I/O or subprocess for valid test cases","AST-based validation catches syntax errors but not semantic errors (infinite loops, type mismatches)","Sanitization patterns must be manually maintained as new unsafe patterns are discovered","Dynamic code generation (eval, exec) cannot be statically analyzed and may bypass sanitization"],"requires":["Python 3.9+ with ast module","Configured sanitization rules (blacklist of unsafe operations)","Generated code as string input"],"input_types":["generated Python code string","sanitization policy configuration"],"output_types":["sanitized code string","validation status (pass/fail)","list of detected unsafe patterns or syntax errors"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_4","uri":"capability://data.processing.analysis.dataset.management.with.task.splits.and.difficulty.stratification","name":"dataset management with task splits and difficulty stratification","description":"Manages a curated dataset of 1,140 programming tasks organized into two splits (Complete for all models, Instruct for instruction-tuned models) and two difficulty subsets (full benchmark, hard subset with 148 challenging tasks). Each task includes docstrings, natural language instructions, test cases, and metadata enabling stratified evaluation across model types and difficulty levels.","intents":["Load and filter tasks by split (Complete vs Instruct) to match model capabilities","Evaluate models on difficulty-stratified subsets to identify performance cliffs","Access task metadata (function signatures, test cases, expected outputs) for custom evaluation","Reproduce benchmark results using identical task definitions across runs"],"best_for":["Researchers running standardized benchmarks with reproducible task sets","Teams analyzing model performance across difficulty levels","Organizations building custom evaluation pipelines on top of BigCodeBench tasks"],"limitations":["Fixed task set may become outdated as libraries evolve (NumPy, Pandas API changes)","Task selection bias toward certain domains (data manipulation, visualization) may not represent all programming","Hard subset (148 tasks) may be too small for statistical significance in some analyses","No mechanism for adding custom tasks without forking the repository"],"requires":["BigCodeBench repository cloned locally","Python 3.9+ with pandas for dataset manipulation","Access to task JSON files in data/ directory"],"input_types":["split selection (complete, instruct)","subset selection (full, hard)","optional task filtering criteria"],"output_types":["task list with docstrings, instructions, test cases","task metadata (function signature, library dependencies)","task identifiers for result tracking"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_5","uri":"capability://data.processing.analysis.result.aggregation.and.pass.k.metric.computation","name":"result aggregation and pass@k metric computation","description":"Aggregates per-task execution results into statistical pass@k metrics that estimate the probability at least one of k generated samples passes all test cases. The system computes pass@1, pass@10, pass@100 from raw execution results, handles edge cases (fewer than k samples generated), and produces leaderboard-formatted output for model comparison.","intents":["Compute pass@k metrics from raw execution results for model comparison","Generate leaderboard rankings based on pass@k performance","Analyze performance variance across different k values (pass@1 vs pass@100)","Export results in standardized format for publication and reproducibility"],"best_for":["Researchers publishing benchmark results with standardized metrics","Teams maintaining public leaderboards of model performance","Organizations comparing models using industry-standard pass@k evaluation"],"limitations":["Pass@k assumes independence between samples, which may not hold if model outputs are correlated","Metric is sensitive to test case quality; weak tests inflate pass@k scores","Does not distinguish between models that pass 1% vs 99% of tasks at pass@k threshold","Requires generating k samples per task, creating quadratic cost scaling with k"],"requires":["Per-task execution results (pass/fail for each sample)","Number of samples generated per task (k)","Evaluation results in JSON format"],"input_types":["evaluation results JSON with per-task pass/fail status","k values for metric computation (1, 10, 100)","optional task metadata for stratified analysis"],"output_types":["pass@k metrics (JSON with pass@1, pass@10, pass@100)","leaderboard-formatted CSV with model rankings","per-task breakdown for detailed analysis"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_6","uri":"capability://automation.workflow.cli.driven.evaluation.workflow.with.modular.commands","name":"cli-driven evaluation workflow with modular commands","description":"Exposes four main CLI commands (generate, evaluate, syncheck, inspect) that decompose the benchmark workflow into discrete, composable steps. Users can generate code samples, validate syntax, execute evaluations, and analyze results independently, enabling partial re-runs, debugging, and custom pipeline construction without re-generating all samples.","intents":["Run benchmark evaluation end-to-end with a single command","Generate code samples once and re-evaluate with different execution backends","Validate code syntax before expensive sandbox execution","Inspect and analyze results without re-running evaluations"],"best_for":["Researchers iterating on evaluation pipelines","Teams debugging model outputs and execution failures","Organizations building custom evaluation workflows on top of BigCodeBench"],"limitations":["CLI interface requires manual orchestration of multi-step workflows; no built-in DAG scheduling","State management between commands relies on file system conventions; no centralized result store","Error handling and retry logic must be implemented by users for robust pipelines","No built-in parallelization; users must manually distribute commands across machines"],"requires":["Python 3.9+ with bigcodebench package installed","Proper environment variables set (API keys, execution backend config)","Write access to bcb_results/ directory for result storage"],"input_types":["command name (generate, evaluate, syncheck, inspect)","model identifier and configuration","task split and subset selection"],"output_types":["generated code samples (JSONL format)","evaluation results (JSON with pass/fail status)","syntax validation reports","aggregated metrics and leaderboard data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_7","uri":"capability://data.processing.analysis.result.persistence.and.result.analysis.with.structured.output.formats","name":"result persistence and result analysis with structured output formats","description":"Persists generated code samples and evaluation results to disk using structured formats (JSONL for samples, JSON for metrics) organized by model, split, backend, and temperature. The system maintains consistent file naming conventions enabling result tracking, comparison, and re-analysis without re-running evaluations.","intents":["Store generated code samples for offline analysis and debugging","Persist evaluation results for reproducibility and long-term tracking","Compare results across multiple model runs using consistent file naming","Analyze failure modes by examining generated code and execution errors"],"best_for":["Researchers maintaining benchmark result archives","Teams analyzing model outputs post-evaluation","Organizations tracking model performance over time"],"limitations":["File-based storage does not scale to very large result sets (millions of samples); requires database for production use","Naming conventions are implicit; no schema validation for result files","No built-in versioning or result deduplication; users must manage file organization","Result analysis requires custom scripts; no built-in visualization or statistical tools"],"requires":["Write access to bcb_results/ directory","Sufficient disk space for storing samples and results (varies by model and k)","Python 3.9+ for result parsing and analysis"],"input_types":["generated code samples (Python strings)","execution results (pass/fail status, error traces)","model metadata (name, backend, temperature)"],"output_types":["JSONL files with generated code samples","JSON files with evaluation results and metrics","CSV files for leaderboard export"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_8","uri":"capability://automation.workflow.docker.based.e2b.sandbox.template.configuration","name":"docker-based e2b sandbox template configuration","description":"Provides pre-configured Docker templates (e2b.Dockerfile, e2b.toml) for deploying isolated code execution environments via E2B sandbox service. Templates define base image, dependency installation, resource limits, and timeout configuration, enabling reproducible remote execution without manual environment setup.","intents":["Deploy isolated code execution environments for large-scale benchmark runs","Ensure consistent execution environment across distributed evaluation machines","Configure resource limits and timeouts for safe code execution","Reproduce execution environment for debugging and result verification"],"best_for":["Teams running distributed benchmarks across multiple machines","Organizations requiring strong isolation and resource limits","Researchers needing reproducible execution environments"],"limitations":["E2B sandbox introduces ~500ms-1s latency per execution compared to local execution","Docker image building and deployment adds setup complexity","E2B service availability and rate limits may constrain benchmark scale","Template customization requires Docker knowledge; not accessible to non-DevOps users"],"requires":["E2B account and API key","Docker installed locally for building images","Network access to E2B service","e2b.Dockerfile and e2b.toml configuration files"],"input_types":["base Docker image specification","dependency list (Python packages, system libraries)","resource limits (CPU, memory, timeout)"],"output_types":["deployed E2B sandbox environment","sandbox configuration metadata","execution logs and resource usage metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__cap_9","uri":"capability://automation.workflow.task.specific.test.case.execution.and.result.capture","name":"task-specific test case execution and result capture","description":"Executes generated code against embedded test cases for each task, capturing execution results (pass/fail), stdout/stderr output, execution time, and error traces. The system handles test case isolation, timeout enforcement, and exception handling to produce reliable pass/fail verdicts even when code crashes or hangs.","intents":["Determine whether generated code correctly implements task requirements","Capture execution errors and stack traces for failure analysis","Measure code execution time and resource consumption","Provide detailed feedback on why code failed (assertion errors, exceptions, timeouts)"],"best_for":["Evaluating code generation quality with objective pass/fail metrics","Debugging model outputs by examining execution errors","Analyzing performance characteristics of generated code"],"limitations":["Test case quality directly impacts evaluation validity; weak tests produce inflated pass rates","Timeout enforcement may prematurely terminate legitimate long-running code","Floating-point comparison in test assertions may produce false negatives due to precision issues","Test cases cannot capture non-functional requirements (code style, maintainability)"],"requires":["Embedded test cases for each task (assertions, expected outputs)","Execution environment with required dependencies installed","Timeout and resource limit configuration"],"input_types":["generated code string","test case functions and assertions","execution environment configuration"],"output_types":["pass/fail verdict","stdout/stderr output","execution time (milliseconds)","error trace (for failed executions)"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-code-bench__headline","uri":"capability://testing.quality.comprehensive.benchmark.for.evaluating.code.generation.capabilities.of.llms","name":"comprehensive benchmark for evaluating code generation capabilities of llms","description":"Big Code Bench is a detailed benchmarking tool designed to assess the code generation abilities of large language models through realistic programming tasks, emphasizing practical skills and library knowledge.","intents":["best code generation benchmark","code generation evaluation tool for LLMs","how to benchmark code generation models","top benchmarks for assessing programming tasks","realistic code generation tests for AI models"],"best_for":["evaluating LLMs","assessing programming skills"],"limitations":[],"requires":["access to LLMs"],"input_types":["code generation tasks"],"output_types":["benchmark results"],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":63,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM API access (OpenAI, Anthropic, Ollama, or local model)","Execution environment (local, E2B sandbox, or Hugging Face Gradio)","Sufficient compute for generating multiple samples per task","API keys for desired providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","Provider-specific Python SDKs installed","Network access to provider endpoints or local Ollama server running","Model-specific parameter ranges (temperature, top_p, max_tokens)","Configuration file or CLI arguments specifying parameters","Sufficient compute budget for multiple benchmark runs"],"failure_modes":["Pass@k metrics require generating k samples per task, creating computational overhead (1,140 tasks × k samples)","Test case coverage may not capture all edge cases or production-grade code quality concerns","Metrics assume deterministic test execution; 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