extended test case generation with 35x multiplier for python code evaluation, safe isolated execution of untrusted llm-generated code with multi-layer resource guards, code sanitization and normalization for consistent evaluation across llm outputs, multi-backend llm integration for code generation with 8+ provider support, pass@k metric calculation with configurable sample aggregation, performance evaluation via cpu instruction counting with evalperf dataset, structured dataset management with problem metadata and test case organization, command-line evaluation pipeline with end-to-end orchestration, comprehensive result logging and visualization for evaluation analysis, comprehensive-test-result-aggregation-and-reporting

MBPP+

BenchmarkFree

Enhanced Python coding benchmark with rigorous testing.

Open Source

/ 100

10 capabilities

Capabilities10 decomposed

extended test case generation with 35x multiplier for python code evaluation

Medium confidence

Generates augmented test suites for MBPP problems by creating 35x more test cases than the original benchmark through systematic edge-case and boundary-condition generation. The system maintains structured metadata for each problem including base_input (original tests), plus_input (extended tests), contract (input validation constraints), atol (floating-point tolerance), canonical_solution (ground truth), and entry_point (function name). This architectural separation enables rigorous detection of fragile solutions that pass shallow tests but fail on edge cases, addressing the fundamental limitation that original MBPP's ~3 tests per task miss correctness issues.

Solves for

Evaluate code generation models with higher rigor to catch solutions that pass original tests but are fundamentally incorrectIdentify fragile implementations that work on common cases but fail on edge cases and boundary conditionsBenchmark LLM code quality against a more comprehensive test surface than existing datasetsCreate reproducible evaluation datasets that expose model weaknesses in handling corner cases

Best for

ML researchers evaluating code generation models (Codex, GPT-4, Claude, etc.)

Teams building code synthesis systems who need rigorous correctness metrics

Benchmark maintainers seeking to improve evaluation signal beyond shallow test coverage

Requires

Python 3.7+

Access to MBPP+ dataset (378 problems with extended test suites)

Canonical solutions for each problem to establish ground truth behavior

Limitations

Test generation is Python-specific; no support for other languages in MBPP+

Extended tests may have higher variance in execution time, requiring dynamic timeout calculation

Test case generation quality depends on the canonical_solution correctness; bugs in ground truth propagate

What makes it unique

Provides 35x test case multiplier specifically for MBPP (378 tasks) with structured metadata separation (base_input vs plus_input) and input validation contracts, enabling systematic edge-case coverage that original MBPP's ~3 tests per task cannot achieve. Uses canonical_solution ground truth execution to dynamically calibrate timeouts and floating-point tolerances per problem.

vs alternatives

Significantly more rigorous than original MBPP (3→105 tests per task average) and HumanEval+ (80x multiplier) while maintaining Python-specific focus; catches correctness issues that shallow benchmarks miss but requires more computational resources for evaluation.

safe isolated execution of untrusted llm-generated code with multi-layer resource guards

Medium confidence

Executes arbitrary Python code generated by LLMs in isolated processes with enforced resource limits and system call restrictions to prevent malicious or buggy code from crashing the evaluation framework. The untrusted_check function spawns separate processes via multiprocessing with shared memory IPC, applies memory limits (default 4GB via EVALPLUS_MAX_MEMORY_BYTES environment variable), dynamically calculated time limits based on ground truth execution time, I/O suppression via swallow_io to prevent output pollution, and reliability_guard to disable dangerous system calls. This architecture prevents code injection, infinite loops, memory exhaustion, and filesystem access while maintaining execution fidelity for correctness evaluation.

Solves for

Safely execute untrusted code from LLM models without risking framework stability or securityIsolate test execution failures so one buggy solution doesn't crash the entire evaluation runMeasure code correctness without interference from stdout/stderr pollution or side effectsEnforce resource constraints to prevent denial-of-service attacks via infinite loops or memory bombs

Best for

Evaluation frameworks processing code from untrusted sources (LLM outputs, user submissions)

CI/CD pipelines that need to safely execute generated code without manual review

Researchers benchmarking multiple models where some outputs may be adversarial or buggy

Requires

Python 3.7+ with multiprocessing support

Linux/Unix OS (resource guards and per-process memory limits are Unix-specific)

EVALPLUS_MAX_MEMORY_BYTES environment variable (optional; defaults to 4GB)

Limitations

Process isolation adds ~50-200ms overhead per execution due to IPC and process spawning

Memory limits are coarse-grained (per-process, not per-function); cannot track memory per code block

Time limits are dynamically calculated from canonical_solution, which may be suboptimal for slow reference implementations

What makes it unique

Implements multi-layer isolation using process-level separation (multiprocessing), memory limits (EVALPLUS_MAX_MEMORY_BYTES), dynamic timeout calculation from canonical_solution execution, I/O suppression (swallow_io), and system call restrictions (reliability_guard). This combination prevents both accidental crashes and intentional attacks while maintaining execution fidelity for correctness evaluation.

vs alternatives

More robust than simple try-catch approaches because it uses OS-level process isolation rather than Python-level exception handling; prevents infinite loops and memory exhaustion that would crash a single-process evaluator, though with higher latency than in-process execution.

code sanitization and normalization for consistent evaluation across llm outputs

Medium confidence

Preprocesses LLM-generated code to normalize formatting, remove extraneous content, and extract the target function before execution. The sanitize module (evalplus/sanitize.py) handles variable formatting inconsistencies, removes comments and docstrings that may interfere with parsing, extracts the function matching the entry_point name, and validates syntax before execution. This ensures that evaluation results reflect code correctness rather than formatting quirks or LLM hallucinations like extra imports or wrapper code. The sanitization pipeline is essential because different LLMs produce code with different indentation, naming conventions, and structural patterns that would otherwise cause false negatives.

Solves for

Normalize code from different LLM models so evaluation is fair and consistent across providersExtract the target function from LLM outputs that may include imports, helper functions, or explanatory textRemove formatting variations (indentation, whitespace, comments) that don't affect correctness but break parsingValidate code syntax before execution to provide clear error messages for malformed outputs

Best for

Evaluation pipelines comparing multiple LLM models with different output formatting conventions

Benchmarks that need to isolate correctness evaluation from code style or presentation

Researchers studying how LLM output quality varies across models and prompts

Requires

Python 3.7+

AST parsing library (built-in ast module)

Valid Python syntax in the input code (after sanitization)

Limitations

Sanitization may remove legitimate code (e.g., necessary imports or helper functions) if they're not recognized as part of the target function

Cannot handle code with syntax errors; requires valid Python syntax after sanitization

Docstring removal may lose important context about function behavior, though this doesn't affect execution

What makes it unique

Implements multi-stage sanitization pipeline that separates formatting normalization (indentation, whitespace) from structural extraction (entry_point function isolation) and validation (syntax checking). Uses AST-based function extraction rather than regex, ensuring robust handling of complex code structures and nested functions.

vs alternatives

More robust than simple regex-based extraction because it uses Python's ast module for structural parsing; handles edge cases like nested functions, decorators, and complex indentation that regex approaches would miss. Enables fair comparison across LLM models with different output conventions.

multi-backend llm integration for code generation with 8+ provider support

Medium confidence

Provides unified interface to generate code from 8+ LLM backends including vLLM, HuggingFace, OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Ollama. The provider architecture (evalplus/provider/) abstracts backend-specific API details behind a common interface, handling authentication, request formatting, response parsing, and error handling for each provider. This enables researchers to benchmark code generation across different models and providers without rewriting evaluation code. The codegen module (evalplus/codegen.py) orchestrates the generation pipeline: problem specification → prompt formatting → LLM call → response extraction → sanitization → evaluation.

Solves for

Benchmark code generation quality across multiple LLM models and providers in a single evaluation runSwitch between local models (vLLM, Ollama) and cloud APIs (OpenAI, Anthropic, Gemini) without code changesGenerate multiple code samples per problem (pass@k evaluation) by calling the same model multiple timesIntegrate custom LLM providers by implementing the provider interface

Best for

Researchers comparing code generation capabilities across OpenAI, Anthropic, Google, and open-source models

Teams evaluating whether to use cloud APIs or self-hosted models for code generation

Benchmark maintainers who need to support multiple LLM backends without duplicating integration code

Requires

Python 3.7+

Provider-specific credentials: OpenAI API key, Anthropic API key, Google Cloud credentials, AWS credentials, HuggingFace token, or local vLLM/Ollama server

Network access to cloud APIs or local server for self-hosted models

Limitations

Provider implementations are tightly coupled to each API's authentication and request format; adding new providers requires code changes

Rate limiting and quota management are provider-specific; no unified rate limiting across backends

Response parsing assumes specific output formats; LLM hallucinations or unexpected formats may break parsing

What makes it unique

Implements provider abstraction layer that unifies 8+ LLM backends (vLLM, HuggingFace, OpenAI, Anthropic, Gemini, Bedrock, Ollama) behind a common interface, enabling single-codebase evaluation across local and cloud models. Each provider handles authentication, request formatting, and response parsing independently, allowing researchers to swap backends without modifying evaluation logic.

vs alternatives

More comprehensive than single-provider frameworks (e.g., OpenAI-only evaluators) because it supports both cloud APIs and self-hosted models; enables cost-benefit analysis between providers and avoids vendor lock-in. Abstraction layer reduces code duplication compared to implementing each provider separately.

pass@k metric calculation with configurable sample aggregation

Medium confidence

Computes pass@k metrics by generating multiple code samples per problem and calculating the probability that at least one sample passes all tests. The metric is calculated as: pass@k = 1 - (C(n-c, k) / C(n, k)) where n is total samples, c is passing samples, and k is the sample count. This enables evaluation of model reliability: pass@1 measures single-shot accuracy, while pass@10 or pass@100 measures whether the model can eventually generate correct code. The framework aggregates results across all problems to produce dataset-level pass@k scores, enabling comparison of models' code generation reliability.

Solves for

Measure code generation model reliability by evaluating multiple samples per problemCompare models fairly using pass@k metrics that account for sampling varianceDetermine whether a model can eventually generate correct code even if single-shot accuracy is lowTrack improvement in model capability as sample count increases (pass@1 → pass@10 → pass@100)

Best for

Researchers benchmarking code generation models where single-shot accuracy is insufficient

Teams evaluating whether to use best-of-n sampling or iterative refinement strategies

Model developers tracking how model improvements affect pass@k across different sample counts

Requires

Multiple code samples per problem (typically 1-100 samples)

Test execution results for each sample (pass/fail per test case)

Sample count k (typically 1, 10, 25, 100)

Limitations

Pass@k assumes independence between samples, which may not hold if the model produces correlated errors

Requires generating k samples per problem, multiplying evaluation cost by k (e.g., 10x cost for pass@10)

Pass@k is less interpretable than pass@1 for users who need single-shot accuracy guarantees

What makes it unique

Implements pass@k metric using combinatorial formula (1 - C(n-c,k)/C(n,k)) rather than empirical sampling, enabling exact calculation without Monte Carlo approximation. Supports configurable k values and aggregation across problems, enabling multi-level analysis (per-problem, per-category, dataset-wide).

vs alternatives

More statistically rigorous than simple accuracy metrics because it accounts for sampling variance and model reliability; enables fair comparison between models with different single-shot accuracy but similar pass@k. Combinatorial calculation is faster and more precise than empirical sampling approaches.

performance evaluation via cpu instruction counting with evalperf dataset

Medium confidence

Measures code efficiency using CPU instruction counting rather than wall-clock time, enabling reproducible performance evaluation across different hardware. The EvalPerf dataset generates performance-exercising inputs with exponential scaling (2^1 to 2^26 elements) to stress-test algorithmic complexity. The profiling pipeline uses Linux perf counters to measure CPU instructions, filters tasks based on profile size, compute cost, coefficient of variation, and performance clustering to select representative benchmarks. This approach isolates algorithmic efficiency from hardware variance, enabling rigorous comparison of code quality across models and implementations.

Solves for

Evaluate code efficiency beyond correctness, measuring algorithmic quality and implementation optimizationCompare models on performance metrics that are reproducible across different hardwareIdentify inefficient implementations that pass correctness tests but have poor algorithmic complexityBenchmark code generation models on both correctness (pass@k) and efficiency (instruction count)

Best for

Researchers evaluating code generation models on both correctness and efficiency

Teams building systems where code performance matters (e.g., competitive programming, embedded systems)

Benchmark maintainers seeking to measure algorithmic quality beyond functional correctness

Requires

Linux OS with perf counter support (Linux 2.6.31+)

Python 3.7+

EvalPerf dataset (subset of MBPP+ with performance benchmarks)

Limitations

Requires Linux with perf counter support; not available on Windows or macOS

CPU instruction counting is hardware-specific; results may vary across CPU architectures

Exponential scaling (2^1 to 2^26) may cause timeout or memory issues for inefficient algorithms

What makes it unique

Uses CPU instruction counting via Linux perf counters rather than wall-clock time, enabling reproducible performance evaluation independent of hardware variance. Generates performance-exercising inputs with exponential scaling (2^1 to 2^26) to stress-test algorithmic complexity, and filters tasks based on profile size, compute cost, and coefficient of variation to select representative benchmarks.

vs alternatives

More reproducible than wall-clock timing because instruction counts are hardware-independent; enables fair comparison across different machines and cloud environments. Exponential input scaling reveals algorithmic complexity issues that constant-size inputs would miss, providing deeper insight into code quality.

structured dataset management with problem metadata and test case organization

Medium confidence

Organizes MBPP+ problems as structured JSON with metadata fields: base_input (original test cases), plus_input (extended test cases), contract (input validation constraints), atol (floating-point tolerance), canonical_solution (ground truth implementation), and entry_point (function name). The dataset management system (evalplus/data/) loads problems from JSON, validates metadata consistency, and provides programmatic access to test cases and solutions. This structured approach enables systematic evaluation: problems can be filtered by category, difficulty, or test coverage; test cases can be aggregated across base and plus inputs; and metadata enables reproducible evaluation across different tools and frameworks.

Solves for

Load and access MBPP+ problems programmatically with consistent metadata structureFilter problems by category, difficulty, or test coverage for targeted evaluationAggregate test results across base_input and plus_input for comprehensive correctness assessmentExport evaluation results in standardized formats for comparison and publication

Best for

Researchers building custom evaluation pipelines that need programmatic access to MBPP+ problems

Benchmark maintainers maintaining and versioning the MBPP+ dataset

Teams integrating MBPP+ into CI/CD pipelines or automated testing systems

Requires

Python 3.7+

MBPP+ dataset files (JSON format)

JSON parsing library (built-in json module)

Limitations

Dataset is Python-specific; no support for other languages

Metadata is static; cannot dynamically generate new test cases or update canonical solutions

JSON format may be inefficient for very large datasets; no built-in compression or indexing

What makes it unique

Implements structured JSON-based dataset organization with explicit separation of base_input (original tests) and plus_input (extended tests), enabling selective evaluation and test coverage analysis. Metadata includes contract (input validation), atol (floating-point tolerance), canonical_solution, and entry_point, providing complete problem specification for reproducible evaluation.

vs alternatives

More structured than flat test files because metadata is explicitly organized and queryable; enables filtering, aggregation, and analysis that would be difficult with unstructured test data. JSON format is human-readable and tool-agnostic, supporting integration with external evaluation frameworks.

command-line evaluation pipeline with end-to-end orchestration

Medium confidence

Provides CLI tools (evalplus.evaluate, evalplus.codegen, evalplus.evalperf, evalplus.sanitize) that orchestrate the complete evaluation workflow: code generation → sanitization → correctness evaluation → optional performance evaluation. The evaluate command executes generated code against MBPP+ test suites with configurable timeouts and memory limits, producing pass@k metrics and detailed result logs. The codegen command generates code from specified LLM providers. The evalperf command measures performance via instruction counting. The sanitize command preprocesses code before evaluation. This modular CLI design enables researchers to run evaluation pipelines without writing custom code, supporting reproducible benchmarking and result sharing.

Solves for

Run complete evaluation pipelines from command line without writing custom Python codeGenerate code from multiple LLM providers and evaluate in a single commandReproduce evaluation results across different machines and environmentsShare evaluation configurations and results with other researchers

Best for

Researchers who prefer CLI tools over programmatic APIs

Teams running evaluation pipelines in CI/CD systems or batch processing

Benchmark maintainers publishing standardized evaluation commands

Requires

Python 3.7+ with evalplus package installed

Provider credentials (API keys, local servers) for code generation

MBPP+ dataset files

Limitations

CLI interface is less flexible than programmatic API; complex workflows require shell scripting

Configuration is command-line arguments or config files; no interactive configuration

Error messages may be cryptic for users unfamiliar with the framework

What makes it unique

Implements modular CLI tools (evaluate, codegen, evalperf, sanitize) that can be chained together or run independently, enabling flexible evaluation workflows. Each tool handles a specific stage of the pipeline (generation, sanitization, evaluation, performance measurement), allowing users to customize workflows without writing code.

vs alternatives

More user-friendly than programmatic APIs for researchers who prefer command-line tools; enables reproducible evaluation without custom code. Modular design allows selective use of components (e.g., evaluate without codegen) for flexibility.

comprehensive result logging and visualization for evaluation analysis

Medium confidence

Captures detailed execution logs including per-problem pass/fail status, execution times, error messages, resource usage (memory, CPU), and pass@k metrics. Results are exported in structured formats (JSON, CSV) enabling downstream analysis, visualization, and comparison. The logging system tracks execution metadata (model name, provider, generation parameters, timestamp) alongside correctness and performance metrics, enabling reproducible result tracking and publication. Visualization utilities generate comparison tables, pass@k curves, and per-category breakdowns, supporting research communication and model comparison.

Solves for

Track detailed evaluation results for reproducibility and publicationCompare models across multiple metrics (pass@k, performance, resource usage)Visualize evaluation results for research papers and presentationsDebug evaluation failures by examining detailed execution logs

Best for

Researchers publishing evaluation results and needing reproducible logging

Teams comparing multiple models and needing detailed comparison tables

Benchmark maintainers tracking evaluation results over time

Requires

Python 3.7+

Disk space for result logs (~1-10MB per evaluation run)

Optional: pandas, matplotlib for advanced analysis and visualization

Limitations

Log files can be large for large-scale evaluations (100s of models × 1000s of problems); requires disk space management

Visualization utilities are basic; complex analysis requires external tools (matplotlib, pandas)

No built-in statistical significance testing; requires external analysis

What makes it unique

Implements comprehensive logging that captures execution metadata (model, provider, parameters, timestamp) alongside correctness and performance metrics, enabling reproducible result tracking and publication. Exports results in structured formats (JSON, CSV) with built-in visualization utilities for comparison tables and pass@k curves.

vs alternatives

More comprehensive than simple pass/fail tracking because it logs execution times, error messages, and resource usage; enables debugging and detailed analysis. Structured export formats support integration with external analysis tools and publication workflows.

comprehensive-test-result-aggregation-and-reporting

Medium confidence

Aggregates execution results across all 378 problems and k samples to produce comprehensive benchmark metrics: pass@k scores, per-problem pass/fail results, sample-level execution details (timeout, memory exceeded, exception), and statistical summaries (mean, std dev, confidence intervals). Results are organized hierarchically (benchmark → problem → sample) and exported as structured JSON for further analysis and visualization.

Solves for

I need to aggregate test results across hundreds of problems and samplesI want to produce pass@k metrics and statistical summaries for benchmark reportingI need to identify which problems are hardest and which samples failed

Best for

benchmark maintainers producing leaderboard results

researchers analyzing model performance across problem categories

teams generating benchmark reports and visualizations

Requires

Python 3.8+

Complete execution results for all problems and samples

Problem metadata (for organizing results)

Limitations

Aggregation assumes all problems are equally weighted; no support for weighted metrics

Statistical summaries are basic (mean, std dev); no advanced statistical analysis

No built-in visualization; results are JSON only; requires external tools for charts/graphs

What makes it unique

Aggregates execution results hierarchically (benchmark → problem → sample) with detailed error classification (timeout, memory exceeded, exception) and produces pass@k metrics across extended test suites (35x more tests than original MBPP). Exports structured JSON results enabling downstream analysis and visualization.

vs alternatives

More detailed than simple pass/fail counting by including error classification and per-sample execution details; more structured than flat result lists by organizing results hierarchically; enables fine-grained analysis of model failures.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

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Prompt36

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ai-generated test case synthesis and supplementationcode execution and test validation with error capture

2 shared capabilities

Model58

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code generation and interpreter security evaluation

1 shared capability

Benchmark63

HumanEval

OpenAI's code generation benchmark — 164 Python problems with unit tests, pass@k evaluation.

sandboxed code execution with timeout and resource limits

1 shared capability

Agent22

GPT Runner

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code execution and validation with sandboxing

1 shared capability

Agent26

Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research

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unrestricted-code-generation-including-malicious

1 shared capability

Benchmark64

ZeroEval

Zero-shot LLM evaluation for reasoning tasks.

code generation task evaluation

1 shared capability

Best For

✓ML researchers evaluating code generation models (Codex, GPT-4, Claude, etc.)
✓Teams building code synthesis systems who need rigorous correctness metrics
✓Benchmark maintainers seeking to improve evaluation signal beyond shallow test coverage
✓Evaluation frameworks processing code from untrusted sources (LLM outputs, user submissions)
✓CI/CD pipelines that need to safely execute generated code without manual review
✓Researchers benchmarking multiple models where some outputs may be adversarial or buggy
✓Evaluation pipelines comparing multiple LLM models with different output formatting conventions
✓Benchmarks that need to isolate correctness evaluation from code style or presentation

Known Limitations

⚠Test generation is Python-specific; no support for other languages in MBPP+
⚠Extended tests may have higher variance in execution time, requiring dynamic timeout calculation
⚠Test case generation quality depends on the canonical_solution correctness; bugs in ground truth propagate
⚠Floating-point comparison requires manual atol specification per problem, adding maintenance overhead
⚠Process isolation adds ~50-200ms overhead per execution due to IPC and process spawning
⚠Memory limits are coarse-grained (per-process, not per-function); cannot track memory per code block

Requirements

Python 3.7+Access to MBPP+ dataset (378 problems with extended test suites)Canonical solutions for each problem to establish ground truth behaviorPython 3.7+ with multiprocessing supportLinux/Unix OS (resource guards and per-process memory limits are Unix-specific)EVALPLUS_MAX_MEMORY_BYTES environment variable (optional; defaults to 4GB)Ground truth execution times for dynamic timeout calculationAST parsing library (built-in ast module)

Input / Output

Accepts: Python function implementations (as strings or AST), Problem specifications with entry_point function names, Input validation contracts and tolerance parameters, Python function code as strings, Test inputs (arguments to pass to the function), Expected outputs for comparison, Timeout and memory limit parameters, Raw LLM-generated code (as strings), Entry point function name to extract, Optional: expected function signature for validation, Problem specification (description, entry_point, canonical_solution, test cases), Model name/identifier (e.g., 'gpt-4', 'claude-3-opus', 'meta-llama/Llama-2-7b'), Generation parameters (temperature, max_tokens, top_p, etc.), Number of samples to generate (for pass@k evaluation), Execution results for multiple samples per problem (pass/fail boolean), Sample count k (integer), Problem specifications (for aggregation across dataset), Python function implementations, Performance-exercising inputs (generated with exponential scaling), Problem specifications with entry_point and canonical_solution, MBPP+ dataset files (JSON), Problem IDs or names for filtering, Optional: category or difficulty filters, Command-line arguments (model name, provider, problem IDs, etc.), Configuration files (JSON or YAML with evaluation parameters), Code files or directories to evaluate, Problem specifications (from MBPP+ dataset), Execution results (pass/fail per problem, execution times, error messages), Model metadata (name, provider, generation parameters), Evaluation configuration (timeouts, memory limits, sample counts), per-sample execution results (pass/fail, error type, execution time), problem identifiers

Produces: Test execution results (pass/fail per test case), Structured test metadata (base_input, plus_input, contract, atol), Pass@k metrics aggregated across test suites, Execution result (pass/fail/timeout/memory-exceeded/error), Actual output from the function, Execution time and resource usage metrics, Error messages and stack traces (if execution failed), Sanitized, executable Python code (as string), Extracted function definition, Validation status (syntax-valid, entry-point-found, etc.), Error messages if sanitization fails, Generated code (as string), Model metadata (name, provider, generation parameters), Generation metrics (tokens used, latency, cost if tracked), Multiple samples per problem (for pass@k calculation), Pass@k score (float 0-1) per problem, Aggregated pass@k score across dataset, Per-model pass@k curves (pass@1, pass@10, pass@25, pass@100, etc.), Comparison tables across models, CPU instruction count (via perf counters), Execution time (wall-clock), Memory usage, Performance metrics (instructions per operation, complexity classification), Efficiency comparison across models, Problem objects with metadata (base_input, plus_input, contract, atol, canonical_solution, entry_point), Test case lists (aggregated or separated), Problem statistics (count, coverage, difficulty distribution), Exported results in standardized formats (JSON, CSV, etc.), Evaluation results (pass/fail per problem, pass@k metrics), Detailed logs (execution times, error messages, resource usage), Result files (JSON, CSV) for further analysis, Performance metrics (if evalperf is enabled), Structured result files (JSON, CSV), Comparison tables (models × metrics), Pass@k curves (sample count vs pass rate), Per-category breakdowns (problem category × metric), Visualization plots (if matplotlib is available), pass@k scores (float 0.0-1.0), per-problem results (pass/fail), statistical summaries (mean, std dev), JSON report with hierarchical structure

UnfragileRank

Adoption70%(25% weight)

Quality90%(35% weight)

Ecosystem40%(15% weight)

Match Graph25%(20% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Benchmark

10 capabilities

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About

Enhanced version of the Mostly Basic Python Problems benchmark with 35x more test cases per problem, providing rigorous evaluation of code generation models by catching solutions that pass original tests but are incorrect.

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Capabilities10 decomposed

extended test case generation with 35x multiplier for python code evaluation

Medium confidence

Solves for

Best for

ML researchers evaluating code generation models (Codex, GPT-4, Claude, etc.)

Teams building code synthesis systems who need rigorous correctness metrics

Benchmark maintainers seeking to improve evaluation signal beyond shallow test coverage

Requires

Python 3.7+

Access to MBPP+ dataset (378 problems with extended test suites)

Canonical solutions for each problem to establish ground truth behavior

Limitations

Test generation is Python-specific; no support for other languages in MBPP+

Extended tests may have higher variance in execution time, requiring dynamic timeout calculation

Test case generation quality depends on the canonical_solution correctness; bugs in ground truth propagate

What makes it unique

vs alternatives

safe isolated execution of untrusted llm-generated code with multi-layer resource guards

Medium confidence

Solves for

Best for

Evaluation frameworks processing code from untrusted sources (LLM outputs, user submissions)

CI/CD pipelines that need to safely execute generated code without manual review

Researchers benchmarking multiple models where some outputs may be adversarial or buggy

Requires

Python 3.7+ with multiprocessing support

Linux/Unix OS (resource guards and per-process memory limits are Unix-specific)

EVALPLUS_MAX_MEMORY_BYTES environment variable (optional; defaults to 4GB)

Limitations

Process isolation adds ~50-200ms overhead per execution due to IPC and process spawning

Memory limits are coarse-grained (per-process, not per-function); cannot track memory per code block

Time limits are dynamically calculated from canonical_solution, which may be suboptimal for slow reference implementations

What makes it unique

vs alternatives

code sanitization and normalization for consistent evaluation across llm outputs

Medium confidence

Solves for

Best for

Evaluation pipelines comparing multiple LLM models with different output formatting conventions

Benchmarks that need to isolate correctness evaluation from code style or presentation

Researchers studying how LLM output quality varies across models and prompts

Requires

Python 3.7+

AST parsing library (built-in ast module)

Valid Python syntax in the input code (after sanitization)

Limitations

Sanitization may remove legitimate code (e.g., necessary imports or helper functions) if they're not recognized as part of the target function

Cannot handle code with syntax errors; requires valid Python syntax after sanitization

Docstring removal may lose important context about function behavior, though this doesn't affect execution

What makes it unique

vs alternatives

multi-backend llm integration for code generation with 8+ provider support

Medium confidence

Solves for

Best for

Researchers comparing code generation capabilities across OpenAI, Anthropic, Google, and open-source models

Teams evaluating whether to use cloud APIs or self-hosted models for code generation

Benchmark maintainers who need to support multiple LLM backends without duplicating integration code

Requires

Python 3.7+

Provider-specific credentials: OpenAI API key, Anthropic API key, Google Cloud credentials, AWS credentials, HuggingFace token, or local vLLM/Ollama server

Network access to cloud APIs or local server for self-hosted models

Limitations

Provider implementations are tightly coupled to each API's authentication and request format; adding new providers requires code changes

Rate limiting and quota management are provider-specific; no unified rate limiting across backends

Response parsing assumes specific output formats; LLM hallucinations or unexpected formats may break parsing

What makes it unique

vs alternatives

pass@k metric calculation with configurable sample aggregation

Medium confidence

Solves for

Best for

Researchers benchmarking code generation models where single-shot accuracy is insufficient

Teams evaluating whether to use best-of-n sampling or iterative refinement strategies

Model developers tracking how model improvements affect pass@k across different sample counts

Requires

Multiple code samples per problem (typically 1-100 samples)

Test execution results for each sample (pass/fail per test case)

Sample count k (typically 1, 10, 25, 100)

Limitations

Pass@k assumes independence between samples, which may not hold if the model produces correlated errors

Requires generating k samples per problem, multiplying evaluation cost by k (e.g., 10x cost for pass@10)

Pass@k is less interpretable than pass@1 for users who need single-shot accuracy guarantees

What makes it unique

vs alternatives

performance evaluation via cpu instruction counting with evalperf dataset

Medium confidence

Solves for

Best for

Researchers evaluating code generation models on both correctness and efficiency

Teams building systems where code performance matters (e.g., competitive programming, embedded systems)

Benchmark maintainers seeking to measure algorithmic quality beyond functional correctness

Requires

Linux OS with perf counter support (Linux 2.6.31+)

Python 3.7+

EvalPerf dataset (subset of MBPP+ with performance benchmarks)

Limitations

Requires Linux with perf counter support; not available on Windows or macOS

CPU instruction counting is hardware-specific; results may vary across CPU architectures

Exponential scaling (2^1 to 2^26) may cause timeout or memory issues for inefficient algorithms

What makes it unique

vs alternatives

structured dataset management with problem metadata and test case organization

Medium confidence

Solves for

Best for

Researchers building custom evaluation pipelines that need programmatic access to MBPP+ problems

Benchmark maintainers maintaining and versioning the MBPP+ dataset

Teams integrating MBPP+ into CI/CD pipelines or automated testing systems

Requires

Python 3.7+

MBPP+ dataset files (JSON format)

JSON parsing library (built-in json module)

Limitations

Dataset is Python-specific; no support for other languages

Metadata is static; cannot dynamically generate new test cases or update canonical solutions

JSON format may be inefficient for very large datasets; no built-in compression or indexing

What makes it unique

vs alternatives

command-line evaluation pipeline with end-to-end orchestration

Medium confidence

Solves for

Best for

Researchers who prefer CLI tools over programmatic APIs

Teams running evaluation pipelines in CI/CD systems or batch processing

Benchmark maintainers publishing standardized evaluation commands

Requires

Python 3.7+ with evalplus package installed

Provider credentials (API keys, local servers) for code generation

MBPP+ dataset files

Limitations

CLI interface is less flexible than programmatic API; complex workflows require shell scripting

Configuration is command-line arguments or config files; no interactive configuration

Error messages may be cryptic for users unfamiliar with the framework

What makes it unique

vs alternatives

comprehensive result logging and visualization for evaluation analysis

Medium confidence

Solves for

Best for

Researchers publishing evaluation results and needing reproducible logging

Teams comparing multiple models and needing detailed comparison tables

Benchmark maintainers tracking evaluation results over time

Requires

Python 3.7+

Disk space for result logs (~1-10MB per evaluation run)

Optional: pandas, matplotlib for advanced analysis and visualization

Limitations

Log files can be large for large-scale evaluations (100s of models × 1000s of problems); requires disk space management

Visualization utilities are basic; complex analysis requires external tools (matplotlib, pandas)

No built-in statistical significance testing; requires external analysis

What makes it unique

vs alternatives

comprehensive-test-result-aggregation-and-reporting

Medium confidence

Solves for

Best for

benchmark maintainers producing leaderboard results

researchers analyzing model performance across problem categories

teams generating benchmark reports and visualizations

Requires

Python 3.8+

Complete execution results for all problems and samples

Problem metadata (for organizing results)

Limitations

Aggregation assumes all problems are equally weighted; no support for weighted metrics

Statistical summaries are basic (mean, std dev); no advanced statistical analysis

No built-in visualization; results are JSON only; requires external tools for charts/graphs

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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MBPP+

Capabilities10 decomposed

extended test case generation with 35x multiplier for python code evaluation

safe isolated execution of untrusted llm-generated code with multi-layer resource guards

code sanitization and normalization for consistent evaluation across llm outputs

multi-backend llm integration for code generation with 8+ provider support

pass@k metric calculation with configurable sample aggregation

performance evaluation via cpu instruction counting with evalperf dataset

structured dataset management with problem metadata and test case organization

command-line evaluation pipeline with end-to-end orchestration

comprehensive result logging and visualization for evaluation analysis

comprehensive-test-result-aggregation-and-reporting

Related Artifactssharing capabilities

AlphaCodium

Llama Guard 3

HumanEval

GPT Runner

Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research

ZeroEval

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to MBPP+

Are you the builder of MBPP+?

Get the weekly brief

Data Sources

MBPP+

Capabilities10 decomposed

extended test case generation with 35x multiplier for python code evaluation

safe isolated execution of untrusted llm-generated code with multi-layer resource guards

code sanitization and normalization for consistent evaluation across llm outputs

multi-backend llm integration for code generation with 8+ provider support

pass@k metric calculation with configurable sample aggregation

performance evaluation via cpu instruction counting with evalperf dataset

structured dataset management with problem metadata and test case organization

command-line evaluation pipeline with end-to-end orchestration

comprehensive result logging and visualization for evaluation analysis

comprehensive-test-result-aggregation-and-reporting

Related Artifactssharing capabilities

AlphaCodium

Llama Guard 3

HumanEval

GPT Runner

Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research

ZeroEval

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to MBPP+

Are you the builder of MBPP+?

Get the weekly brief

Data Sources