extended-test-case-generation-for-code-problems, safe-isolated-code-execution-with-resource-limits, pass-at-k-metric-calculation-for-code-generation, code-sanitization-and-safety-preprocessing, multi-backend-llm-code-generation-with-provider-abstraction, performance-evaluation-via-cpu-instruction-counting, structured-dataset-management-with-metadata-fields, command-line-evaluation-pipeline-orchestration, batch-code-generation-with-configurable-sampling, comprehensive-test-result-aggregation-and-reporting

MBPP+

DatasetFree

Enhanced Python coding benchmark with rigorous testing.

Open Source

/ 100

10 capabilities

Capabilities10 decomposed

extended-test-case-generation-for-code-problems

Medium confidence

Generates 35x more test cases per problem than the original MBPP benchmark by creating edge-case and boundary-condition tests beyond base inputs. The system uses a contract-based validation approach with input constraints (contract field), floating-point tolerance specifications (atol), and canonical solution execution to derive comprehensive test suites that expose fragile implementations passing only base tests.

Solves for

I need to rigorously evaluate code generation models beyond surface-level correctnessI want to catch solutions that pass original tests but fail on edge cases and boundary conditionsI need a dataset that exposes overfitting to minimal test coverage in code generation benchmarks

Best for

ML researchers evaluating code generation models (GPT, Claude, open-source LLMs)

benchmark designers creating rigorous evaluation suites

teams building code synthesis systems that need ground-truth validation

Requires

Python 3.8+

Original MBPP dataset (378 problems)

Canonical solution implementations for each problem

Limitations

Test case generation is Python-specific; no support for other programming languages

Extended tests increase evaluation runtime by 35x compared to original MBPP

Floating-point tolerance (atol) handling may not cover all numerical precision edge cases across different implementations

What makes it unique

Multiplies test coverage by 35x through systematic generation of plus_input test cases derived from canonical solutions and input contracts, rather than relying on manually curated test suites. Includes atol (absolute tolerance) fields for floating-point comparisons and contract specifications for input validation, enabling detection of solutions that pass base tests but fail on boundary conditions.

vs alternatives

Provides 35x more test cases per problem than original MBPP (35 vs ~3 tests per task), catching incorrect implementations that pass minimal test suites where competitors like HumanEval or raw MBPP would miss them.

safe-isolated-code-execution-with-resource-limits

Medium confidence

Executes untrusted LLM-generated Python code in isolated processes with multi-layer sandboxing: process isolation via multiprocessing, memory limits (default 4GB via EVALPLUS_MAX_MEMORY_BYTES), dynamically calculated time limits based on canonical solution execution time, I/O suppression via swallow_io, and system call guards via reliability_guard. Each sample runs in a separate process with shared memory for inter-process communication.

Solves for

I need to safely execute arbitrary code generated by LLMs without risking system compromiseI want to measure code correctness while preventing infinite loops, memory exhaustion, and malicious system callsI need consistent timeout behavior based on expected execution time of correct solutions

Best for

code evaluation platforms running untrusted LLM-generated code

benchmark evaluation systems requiring safe execution of thousands of code samples

research teams evaluating code generation models in isolated environments

Requires

Python 3.8+

Linux/Unix OS (process isolation via multiprocessing)

EVALPLUS_MAX_MEMORY_BYTES environment variable (optional; default 4GB)

Limitations

Process isolation adds overhead (~50-200ms per execution); not suitable for real-time code execution

Memory limits are global (4GB default) and may be insufficient for memory-intensive algorithms

Time limits are dynamically calculated from canonical solution execution, which may not account for algorithmic complexity differences in generated code

What makes it unique

Combines process isolation, memory limits, dynamic timeout calculation (based on canonical solution execution), I/O suppression, and system call guards in a single execution pipeline. Timeout is not fixed but derived from ground-truth execution time, preventing both premature termination of slow-but-correct solutions and runaway execution of inefficient code.

vs alternatives

More comprehensive than simple timeout-based execution (e.g., raw subprocess calls) by adding memory limits, I/O suppression, and system call guards; more flexible than fixed timeouts by dynamically calibrating to canonical solution performance.

pass-at-k-metric-calculation-for-code-generation

Medium confidence

Calculates pass@k metrics by executing k independent code samples per problem and computing the probability that at least one passes all test cases. Aggregates results across the full problem set to produce benchmark-wide pass@k scores. Supports multiple k values (k=1, 5, 10, etc.) to measure model robustness and sample efficiency.

Solves for

I need to measure code generation model quality using the standard pass@k metricI want to compare models fairly by accounting for sampling variance and generation diversityI need to report model performance across multiple k values to show sample efficiency

Best for

ML researchers publishing code generation benchmarks

model evaluation teams comparing LLM code generation capabilities

leaderboard maintainers tracking model performance over time

Requires

k >= 1 (number of samples per problem)

Complete test suite for each problem (base_input + plus_input)

Execution results for all k samples across all problems

Limitations

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

Pass@k assumes independence of samples; may not hold if model generates similar code across samples

Does not measure code quality beyond correctness (e.g., efficiency, readability, maintainability)

What makes it unique

Implements pass@k calculation across extended test suites (35x more tests than original MBPP), making the metric more stringent and revealing model weaknesses that pass@k on minimal test coverage would miss. Aggregates results across 378 problems with comprehensive test coverage per problem.

vs alternatives

More rigorous than pass@k on original MBPP (which uses ~3 tests per problem) because extended test suites expose fragile solutions; comparable to HumanEval+ but with 2.3x more problems (378 vs 164 tasks).

code-sanitization-and-safety-preprocessing

Medium confidence

Preprocesses LLM-generated code before execution by removing or neutralizing potentially dangerous constructs: strips import statements that could access system resources, removes eval/exec calls, sanitizes file I/O operations, and disables network access. The sanitize.py module applies these transformations while preserving functional code logic, enabling safe execution of untrusted code without manual review.

Solves for

I need to automatically remove dangerous code patterns from LLM outputs before executionI want to prevent code from accessing files, network, or system resources during evaluationI need to sanitize code at scale without manual review of thousands of samples

Best for

code evaluation platforms processing high volumes of LLM-generated code

benchmark systems requiring automated safety preprocessing

research teams evaluating code generation without manual code review

Requires

Python 3.8+

evalplus/sanitize.py module

LLM-generated Python code as input

Limitations

Sanitization is heuristic-based (pattern matching) and may miss sophisticated attack vectors

Over-aggressive sanitization may break legitimate code (e.g., code using standard library imports for math operations)

No semantic analysis; cannot distinguish between dangerous and benign uses of the same construct

What makes it unique

Applies pattern-based sanitization to remove dangerous constructs (imports, eval/exec, file I/O, network access) before execution, complementing process-level isolation. Works in conjunction with reliability_guard system calls filtering to provide defense-in-depth against malicious or accidental harmful code.

vs alternatives

Combines code-level sanitization (removing dangerous constructs) with process-level isolation (memory/time limits, system call guards), providing layered defense; simpler than full AST-based code analysis but faster and more practical for high-volume evaluation.

multi-backend-llm-code-generation-with-provider-abstraction

Medium confidence

Provides unified interface for code generation across 8+ LLM providers (vLLM, HuggingFace, OpenAI, Anthropic, Google Gemini, AWS Bedrock, Ollama) through a provider abstraction layer. Each provider implements a common interface for prompt submission, sampling, and result retrieval, enabling seamless switching between models without changing evaluation code. Supports batch generation and configurable sampling parameters (temperature, top_p, max_tokens).

Solves for

I want to evaluate multiple code generation models without writing provider-specific codeI need to switch between local (vLLM, Ollama) and cloud (OpenAI, Anthropic) models with minimal code changesI want to run large-scale code generation benchmarks across different model providers

Best for

researchers comparing code generation across multiple LLM providers

benchmark maintainers supporting diverse model evaluation

teams building multi-model code generation pipelines

Requires

Python 3.8+

Provider-specific dependencies (vllm, transformers, openai, anthropic, google-generativeai, boto3, ollama)

API keys for cloud providers (OpenAI, Anthropic, Google, AWS)

Limitations

Provider abstraction adds latency (~10-50ms per request) due to interface translation

Not all providers support identical sampling parameters; some parameters may be ignored for certain backends

API rate limits and quota management are provider-specific; no unified rate limiting across providers

What makes it unique

Implements provider abstraction layer supporting 8+ LLM backends (vLLM, HuggingFace, OpenAI, Anthropic, Google Gemini, AWS Bedrock, Ollama) through common interface in evalplus/provider/__init__.py, enabling single evaluation pipeline to work across local and cloud models without code changes. Supports both local inference (vLLM, Ollama) and cloud APIs with unified sampling parameter handling.

vs alternatives

More comprehensive provider support than single-model evaluation frameworks; more flexible than hardcoded provider integrations by using abstraction layer pattern; enables fair comparison across providers by normalizing sampling parameters and result formats.

performance-evaluation-via-cpu-instruction-counting

Medium confidence

Measures code efficiency using CPU instruction counting (via Linux perf) rather than wall-clock time, providing hardware-independent performance metrics. Generates performance-exercising inputs with exponential scaling (2^1 to 2^26) to stress-test algorithms, filters tasks based on profile size and compute cost, and produces EvalPerf dataset with instruction count baselines for each problem.

Solves for

I need to measure code efficiency independent of system load and hardware variationI want to identify algorithmic inefficiencies (e.g., O(n^2) vs O(n log n)) through instruction countingI need to evaluate both correctness and performance of generated code

Best for

researchers evaluating code generation models on both correctness and efficiency

algorithm competition platforms requiring fair performance comparison

teams building code optimization tools that need performance baselines

Requires

Linux OS with perf tool installed

Python 3.8+

Performance-exercising input generation (PE input)

Limitations

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

Instruction counting is CPU-specific; results may vary across different processor architectures

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

What makes it unique

Uses CPU instruction counting via Linux perf instead of wall-clock time, providing hardware-independent performance metrics. Generates exponentially-scaled performance-exercising inputs (2^1 to 2^26) to stress-test algorithms and expose inefficient implementations. Filters tasks based on profile size, compute cost, coefficient of variation, and performance clustering to create manageable EvalPerf dataset.

vs alternatives

More rigorous than wall-clock time measurement (which varies with system load) and more practical than full algorithmic complexity analysis; provides objective hardware-independent performance baseline for comparing generated code efficiency.

structured-dataset-management-with-metadata-fields

Medium confidence

Organizes code problems as structured objects with standardized 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). Provides dataset loading, filtering, and iteration utilities through evalplus/data/__init__.py, enabling programmatic access to 378 MBPP+ problems with consistent schema.

Solves for

I need to load and iterate over MBPP+ problems with consistent metadata structureI want to filter problems by difficulty, input constraints, or other metadataI need to access canonical solutions and test cases for evaluation and analysis

Best for

researchers building custom evaluation pipelines on top of MBPP+

benchmark analysis tools requiring structured problem access

teams creating derived datasets or problem subsets

Requires

Python 3.8+

evalplus/data/__init__.py module

MBPP+ dataset files (JSON format)

Limitations

Schema is fixed; no extensibility for custom metadata fields without modifying core dataset

Dataset is Python-specific; no support for problems in other languages

Metadata fields (contract, atol) are manually specified per problem; no automatic inference

What makes it unique

Provides standardized schema for 378 MBPP+ problems with fields for base/extended test cases (base_input, plus_input), input validation (contract), floating-point tolerance (atol), ground truth (canonical_solution), and function entry point. Enables programmatic dataset access through consistent interface rather than raw JSON files.

vs alternatives

More structured than raw JSON dataset files; provides consistent schema across all problems enabling reliable programmatic access; includes extended test cases (plus_input) and validation constraints (contract) not present in original MBPP.

command-line-evaluation-pipeline-orchestration

Medium confidence

Provides CLI tools (evalplus.evaluate, evalplus.codegen, evalplus.evalperf, evalplus.sanitize) that orchestrate the complete evaluation workflow: code generation from LLM → sanitization → correctness evaluation → optional performance evaluation. Each CLI tool accepts configuration parameters (model, dataset, sampling params) and produces structured output (JSON results, pass@k metrics, performance data). Enables end-to-end benchmark execution without writing custom Python code.

Solves for

I want to run a complete code generation benchmark without writing evaluation codeI need to generate code, evaluate it, and produce results in a single commandI want to configure evaluation parameters (model, dataset, k values) via CLI arguments

Best for

researchers running quick benchmark evaluations

CI/CD pipelines automating model evaluation

non-technical users running pre-configured evaluation workflows

Requires

Python 3.8+

evalplus package installed

LLM provider credentials (API keys for cloud providers)

Limitations

CLI interface is less flexible than programmatic API; complex custom workflows require Python code

Configuration via CLI arguments becomes unwieldy for large parameter sets; no config file support documented

Output format is fixed (JSON); no customization of result format or fields

What makes it unique

Provides four integrated CLI tools (evalplus.codegen, evalplus.evaluate, evalplus.evalperf, evalplus.sanitize) that chain together to form complete evaluation pipeline: generation → sanitization → correctness evaluation → performance evaluation. Each tool accepts configuration parameters and produces structured JSON output, enabling end-to-end benchmark execution from command line.

vs alternatives

More integrated than individual tools (e.g., separate code generation and evaluation scripts); more accessible than programmatic API for non-developers; enables reproducible evaluation workflows via CLI commands.

batch-code-generation-with-configurable-sampling

Medium confidence

Generates multiple code samples per problem with configurable sampling parameters (temperature, top_p, max_tokens, num_samples) through evalplus.codegen CLI and codegen.py module. Supports batch processing across all 378 MBPP+ problems, with results organized by problem ID and sample index. Integrates with multi-provider LLM abstraction to support diverse model backends without code changes.

Solves for

I need to generate k code samples per problem for pass@k evaluationI want to control sampling diversity via temperature and top_p parametersI need to batch-generate code across hundreds of problems efficiently

Best for

researchers generating code samples for pass@k benchmarking

model evaluation teams creating diverse code samples for robustness testing

benchmark maintainers generating baseline results for leaderboards

Requires

Python 3.8+

LLM provider (vLLM, OpenAI, Anthropic, etc.) with API access

MBPP+ dataset

Limitations

Batch generation is sequential per problem; no parallelization across problems

Sampling parameters are global; no per-problem parameter customization

Generation cost scales linearly with num_samples; no caching or deduplication of similar samples

What makes it unique

Integrates with multi-provider LLM abstraction to generate code samples across vLLM, OpenAI, Anthropic, Google, AWS, and Ollama without provider-specific code. Supports configurable sampling (temperature, top_p, max_tokens) and batch processing across 378 problems with results organized by problem ID and sample index.

vs alternatives

More flexible than fixed-temperature generation by supporting configurable sampling parameters; more convenient than manual per-provider code generation by using unified provider abstraction; enables fair comparison across models by normalizing generation parameters.

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

Artifacts that share capabilities with MBPP+, ranked by overlap. Discovered automatically through the match graph.

Dataset48

MBPP (Mostly Basic Python Problems)

974 basic Python problems complementing HumanEval for code evaluation.

test case execution and pass-rate calculationreference solution and test case provision

2 shared capabilities

Dataset48

CodeContests

13K competitive programming problems from AlphaCode research.

test-case-driven-code-evaluation-harness

1 shared capability

Repository34

phantom-lens

A Cluely / Interview Coder alternative with features we probably shouldn’t talk about, built for winning exams..

test case generation and validation against solution code

1 shared capability

Benchmark42

HumanEval

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

functional correctness testing via unit test execution

1 shared capability

Model22

Kwaipilot: KAT-Coder-Pro V2

KAT-Coder-Pro V2 is the latest high-performance model in KwaiKAT’s KAT-Coder series, designed for complex enterprise-grade software engineering and SaaS integration. It builds on the agentic coding strengths of earlier versions,...

test case generation with coverage-aware strategy

1 shared capability

Product18

Mutable AI

AI-Accelerated Software Development

test case generation from code specifications

1 shared capability

Best For

✓ML researchers evaluating code generation models (GPT, Claude, open-source LLMs)
✓benchmark designers creating rigorous evaluation suites
✓teams building code synthesis systems that need ground-truth validation
✓code evaluation platforms running untrusted LLM-generated code
✓benchmark evaluation systems requiring safe execution of thousands of code samples
✓research teams evaluating code generation models in isolated environments
✓ML researchers publishing code generation benchmarks
✓model evaluation teams comparing LLM code generation capabilities

Known Limitations

⚠Test case generation is Python-specific; no support for other programming languages
⚠Extended tests increase evaluation runtime by 35x compared to original MBPP
⚠Floating-point tolerance (atol) handling may not cover all numerical precision edge cases across different implementations
⚠Contract-based validation requires manual specification per problem; not automatically inferred
⚠Process isolation adds overhead (~50-200ms per execution); not suitable for real-time code execution
⚠Memory limits are global (4GB default) and may be insufficient for memory-intensive algorithms

Requirements

Python 3.8+Original MBPP dataset (378 problems)Canonical solution implementations for each problemAccess to evalplus/evalplus repositoryLinux/Unix OS (process isolation via multiprocessing)EVALPLUS_MAX_MEMORY_BYTES environment variable (optional; default 4GB)Canonical solution execution time baseline for timeout calculationk >= 1 (number of samples per problem)

Input / Output

Accepts: problem specifications with base_input test cases, canonical solution code, input validation constraints (contract), Python code strings (generated by LLM), function entry point name, test inputs (arguments to pass to function), execution results (pass/fail) for k samples per problem, problem count (378 for MBPP+), Python code strings (potentially untrusted), problem description (text), prompt template, sampling parameters (temperature, top_p, max_tokens, num_samples), problem code, performance-exercising inputs (exponentially scaled), canonical solution for baseline, dataset identifier (e.g., 'mbpp'), optional filter criteria (problem ID, difficulty), CLI arguments: model name, dataset, sampling parameters, output path, problem specifications (from MBPP+ dataset), sampling parameters: temperature, top_p, max_tokens, num_samples, model name/identifier, per-sample execution results (pass/fail, error type, execution time), problem identifiers

Produces: structured test case objects with base_input and plus_input fields, test execution results with pass/fail metrics, execution result (pass/fail), error messages (timeout, memory exceeded, exception), execution time metrics, pass@k score (float 0.0-1.0), per-problem pass/fail results, aggregated statistics (mean, std dev), sanitized Python code strings, list of removed/modified constructs (optional), generated code samples (list of strings), provider metadata (model name, generation time), instruction count per problem, performance comparison (generated vs canonical), EvalPerf dataset with efficiency metrics, problem objects with fields: base_input, plus_input, contract, atol, canonical_solution, entry_point, iterable dataset for batch processing, JSON results file with pass@k metrics, per-problem execution results, performance data (if evalperf enabled), generated code samples (list per problem), sample metadata (model, temperature, generation time), 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%(35% weight)

Quality23%(25% weight)

Ecosystem40%(20% weight)

Match Graph10%(15% 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: Dataset

10 capabilities

Visit MBPP+→

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-for-code-problems

Medium confidence

Solves for

Best for

ML researchers evaluating code generation models (GPT, Claude, open-source LLMs)

benchmark designers creating rigorous evaluation suites

teams building code synthesis systems that need ground-truth validation

Requires

Python 3.8+

Original MBPP dataset (378 problems)

Canonical solution implementations for each problem

Limitations

Test case generation is Python-specific; no support for other programming languages

Extended tests increase evaluation runtime by 35x compared to original MBPP

Floating-point tolerance (atol) handling may not cover all numerical precision edge cases across different implementations

What makes it unique

vs alternatives

safe-isolated-code-execution-with-resource-limits

Medium confidence

Solves for

Best for

code evaluation platforms running untrusted LLM-generated code

benchmark evaluation systems requiring safe execution of thousands of code samples

research teams evaluating code generation models in isolated environments

Requires

Python 3.8+

Linux/Unix OS (process isolation via multiprocessing)

EVALPLUS_MAX_MEMORY_BYTES environment variable (optional; default 4GB)

Limitations

Process isolation adds overhead (~50-200ms per execution); not suitable for real-time code execution

Memory limits are global (4GB default) and may be insufficient for memory-intensive algorithms

Time limits are dynamically calculated from canonical solution execution, which may not account for algorithmic complexity differences in generated code

What makes it unique

vs alternatives

pass-at-k-metric-calculation-for-code-generation

Medium confidence

Solves for

Best for

ML researchers publishing code generation benchmarks

model evaluation teams comparing LLM code generation capabilities

leaderboard maintainers tracking model performance over time

Requires

k >= 1 (number of samples per problem)

Complete test suite for each problem (base_input + plus_input)

Execution results for all k samples across all problems

Limitations

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

Pass@k assumes independence of samples; may not hold if model generates similar code across samples

Does not measure code quality beyond correctness (e.g., efficiency, readability, maintainability)

What makes it unique

vs alternatives

code-sanitization-and-safety-preprocessing

Medium confidence

Solves for

Best for

code evaluation platforms processing high volumes of LLM-generated code

benchmark systems requiring automated safety preprocessing

research teams evaluating code generation without manual code review

Requires

Python 3.8+

evalplus/sanitize.py module

LLM-generated Python code as input

Limitations

Sanitization is heuristic-based (pattern matching) and may miss sophisticated attack vectors

Over-aggressive sanitization may break legitimate code (e.g., code using standard library imports for math operations)

No semantic analysis; cannot distinguish between dangerous and benign uses of the same construct

What makes it unique

vs alternatives

multi-backend-llm-code-generation-with-provider-abstraction

Medium confidence

Solves for

Best for

researchers comparing code generation across multiple LLM providers

benchmark maintainers supporting diverse model evaluation

teams building multi-model code generation pipelines

Requires

Python 3.8+

Provider-specific dependencies (vllm, transformers, openai, anthropic, google-generativeai, boto3, ollama)

API keys for cloud providers (OpenAI, Anthropic, Google, AWS)

Limitations

Provider abstraction adds latency (~10-50ms per request) due to interface translation

Not all providers support identical sampling parameters; some parameters may be ignored for certain backends

API rate limits and quota management are provider-specific; no unified rate limiting across providers

What makes it unique

vs alternatives

performance-evaluation-via-cpu-instruction-counting

Medium confidence

Solves for

Best for

researchers evaluating code generation models on both correctness and efficiency

algorithm competition platforms requiring fair performance comparison

teams building code optimization tools that need performance baselines

Requires

Linux OS with perf tool installed

Python 3.8+

Performance-exercising input generation (PE input)

Limitations

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

Instruction counting is CPU-specific; results may vary across different processor architectures

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

What makes it unique

vs alternatives

structured-dataset-management-with-metadata-fields

Medium confidence

Solves for

Best for

researchers building custom evaluation pipelines on top of MBPP+

benchmark analysis tools requiring structured problem access

teams creating derived datasets or problem subsets

Requires

Python 3.8+

evalplus/data/__init__.py module

MBPP+ dataset files (JSON format)

Limitations

Schema is fixed; no extensibility for custom metadata fields without modifying core dataset

Dataset is Python-specific; no support for problems in other languages

Metadata fields (contract, atol) are manually specified per problem; no automatic inference

What makes it unique

vs alternatives

command-line-evaluation-pipeline-orchestration

Medium confidence

Solves for

Best for

researchers running quick benchmark evaluations

CI/CD pipelines automating model evaluation

non-technical users running pre-configured evaluation workflows

Requires

Python 3.8+

evalplus package installed

LLM provider credentials (API keys for cloud providers)

Limitations

CLI interface is less flexible than programmatic API; complex custom workflows require Python code

Configuration via CLI arguments becomes unwieldy for large parameter sets; no config file support documented

Output format is fixed (JSON); no customization of result format or fields

What makes it unique

vs alternatives

batch-code-generation-with-configurable-sampling

Medium confidence

Solves for

Best for

researchers generating code samples for pass@k benchmarking

model evaluation teams creating diverse code samples for robustness testing

benchmark maintainers generating baseline results for leaderboards

Requires

Python 3.8+

LLM provider (vLLM, OpenAI, Anthropic, etc.) with API access

MBPP+ dataset

Limitations

Batch generation is sequential per problem; no parallelization across problems

Sampling parameters are global; no per-problem parameter customization

Generation cost scales linearly with num_samples; no caching or deduplication of similar samples

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-for-code-problems

safe-isolated-code-execution-with-resource-limits

pass-at-k-metric-calculation-for-code-generation

code-sanitization-and-safety-preprocessing

multi-backend-llm-code-generation-with-provider-abstraction

performance-evaluation-via-cpu-instruction-counting

structured-dataset-management-with-metadata-fields

command-line-evaluation-pipeline-orchestration

batch-code-generation-with-configurable-sampling

comprehensive-test-result-aggregation-and-reporting

Related Artifactssharing capabilities

MBPP (Mostly Basic Python Problems)

CodeContests

phantom-lens

HumanEval

Kwaipilot: KAT-Coder-Pro V2

Mutable AI

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to MBPP+

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Data Sources

MBPP+

Capabilities10 decomposed

extended-test-case-generation-for-code-problems

safe-isolated-code-execution-with-resource-limits

pass-at-k-metric-calculation-for-code-generation

code-sanitization-and-safety-preprocessing

multi-backend-llm-code-generation-with-provider-abstraction

performance-evaluation-via-cpu-instruction-counting

structured-dataset-management-with-metadata-fields

command-line-evaluation-pipeline-orchestration

batch-code-generation-with-configurable-sampling

comprehensive-test-result-aggregation-and-reporting

Related Artifactssharing capabilities

MBPP (Mostly Basic Python Problems)

CodeContests

phantom-lens

HumanEval

Kwaipilot: KAT-Coder-Pro V2

Mutable AI

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