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Each problem includes a natural language task description, a correct implementation function, and three validation test cases that verify functional correctness. Models generate code solutions which are executed against these test cases to compute pass@k metrics (percentage of problems solved within k attempts).","intents":["Evaluate code generation models on basic programming proficiency tasks","Compare model performance across different architectures and training approaches","Measure improvement in code generation capabilities over time","Benchmark LLM code generation on problems requiring string, list, and mathematical operations"],"best_for":["ML researchers evaluating code generation models","Teams building and fine-tuning code LLMs","Organizations comparing commercial vs open-source code models","Researchers studying code generation on basic algorithmic problems"],"limitations":["Limited to basic Python problems — does not test advanced concepts like async/await, decorators, metaclasses, or complex OOP patterns","Only 974 problems total — relatively small dataset compared to modern code corpora, may not capture long-tail programming patterns","Test cases are minimal (3 per problem) — may not catch edge cases or robustness issues in generated code","No evaluation of code quality metrics like readability, efficiency, or style — only functional correctness","Python-only — cannot evaluate code generation for other languages"],"requires":["Python 3.6+ runtime for executing generated code and test cases","Hugging Face datasets library for loading the benchmark","Code generation model with Python output capability","Test harness to execute generated code and compare against expected outputs"],"input_types":["natural language task descriptions","generated Python code strings"],"output_types":["pass/fail test results per problem","pass@k metrics (pass@1, pass@10, etc.)","execution logs and error traces"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mbpp-mostly-basic-python-problems__cap_1","uri":"capability://data.processing.analysis.multi.problem.code.correctness.validation","name":"multi-problem code correctness validation","description":"Executes generated Python code against a suite of predefined test cases to determine functional correctness at scale. The validation system runs each generated solution through 3 test cases per problem, capturing execution results, exceptions, and output matching. Supports batch evaluation of multiple model outputs across all 974 problems with aggregation of pass rates and failure analysis.","intents":["Validate that generated code produces correct outputs for given inputs","Identify which problem categories or types a model struggles with","Detect runtime errors, exceptions, and incorrect logic in generated solutions","Aggregate correctness metrics across large batches of generated code"],"best_for":["Automated evaluation pipelines for code generation models","Continuous integration systems testing model checkpoints","Researchers analyzing failure modes and error patterns","Teams establishing baseline performance metrics for code models"],"limitations":["Test cases only verify functional correctness — do not evaluate code efficiency, memory usage, or algorithmic complexity","Execution-based validation requires sandboxing to prevent malicious code — standard approach adds latency and resource overhead","Cannot detect subtle bugs that don't manifest in the 3 provided test cases per problem","No timeout protection specified — long-running or infinite-loop code may hang evaluation","Test case coverage is limited — edge cases and boundary conditions may not be represented"],"requires":["Python 3.6+ with ability to execute arbitrary code","Sandboxing mechanism (Docker, subprocess isolation, or similar) for safe code execution","Test harness to parse problem definitions and execute generated code","Timeout mechanism to prevent hanging on infinite loops"],"input_types":["generated Python function code as strings","problem definitions with test case inputs and expected outputs"],"output_types":["boolean pass/fail per test case","execution errors and exception traces","actual vs expected output comparisons","aggregated pass rates and statistics"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mbpp-mostly-basic-python-problems__cap_2","uri":"capability://data.processing.analysis.problem.categorization.and.concept.mapping","name":"problem categorization and concept mapping","description":"Organizes 974 problems into categories based on programming concepts tested: string manipulation, list operations, mathematical functions, and data structure algorithms. Each problem is tagged with the primary concepts it exercises, enabling filtered evaluation and analysis by concept area. This categorization allows researchers to understand model performance on specific programming domains and identify capability gaps.","intents":["Analyze model performance broken down by programming concept or problem category","Identify which programming concepts a model struggles with most","Select subsets of problems for targeted evaluation of specific capabilities","Understand the breadth of programming knowledge a model has acquired"],"best_for":["Researchers analyzing model capabilities across programming domains","Teams evaluating whether models have learned specific programming patterns","Educators using the dataset to understand what concepts models understand","Model developers targeting improvements in weak concept areas"],"limitations":["Categorization is coarse-grained — many problems span multiple concepts but are tagged with only primary concept","No hierarchical taxonomy — cannot distinguish between basic and advanced versions of same concept","Categories are fixed and predefined — cannot dynamically group problems by custom criteria","No difficulty ratings within categories — all string manipulation problems treated as equivalent complexity"],"requires":["Access to problem metadata with concept tags","Ability to filter and group problems by category","Evaluation results mapped back to problem categories"],"input_types":["problem definitions with concept tags"],"output_types":["grouped evaluation results by concept","per-category pass rates and statistics","concept-specific failure analysis"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mbpp-mostly-basic-python-problems__cap_3","uri":"capability://memory.knowledge.reference.solution.and.test.case.repository","name":"reference solution and test case repository","description":"Maintains a curated collection of 974 correct Python implementations paired with their corresponding test cases. Each problem includes a reference solution function that serves as ground truth for correctness evaluation, plus 3 test cases with inputs and expected outputs. This repository enables reproducible evaluation by providing a stable baseline that all generated code is compared against.","intents":["Provide ground truth implementations for validating generated code correctness","Enable reproducible evaluation across different research groups and time periods","Serve as examples of correct Python patterns for specific programming tasks","Support analysis of how generated code differs from reference implementations"],"best_for":["Researchers needing a stable, reproducible evaluation baseline","Teams building code generation evaluation infrastructure","Organizations comparing results across different models and time periods","Educators using correct solutions as teaching examples"],"limitations":["Reference solutions are single implementations — may not represent all valid approaches to a problem","Test cases are minimal (3 per problem) — reference solution may pass tests but not be optimal or idiomatic","No alternative correct solutions provided — generated code using different valid approaches may be marked incorrect","Solutions are fixed — cannot be updated if bugs are discovered without breaking reproducibility","No explanation or comments in reference solutions — difficult to understand the reasoning behind implementation choices"],"requires":["Access to the MBPP dataset with reference solutions and test cases","Python 3.6+ to execute reference solutions","Test harness to run reference solutions against test cases"],"input_types":["problem descriptions and test case specifications"],"output_types":["Python function implementations","test case inputs and expected outputs","execution results from reference solutions"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mbpp-mostly-basic-python-problems__cap_4","uri":"capability://data.processing.analysis.pass.k.metric.computation.and.aggregation","name":"pass@k metric computation and aggregation","description":"Computes pass@k metrics by sampling k generated solutions per problem and checking if at least one passes all test cases. Aggregates results across all 974 problems to produce overall pass@1, pass@10, pass@100 statistics. This metric accounts for the fact that code generation models can produce multiple valid solutions and benefits from sampling multiple attempts.","intents":["Measure code generation model performance using standard industry metrics","Compare models fairly by accounting for sampling variance","Track improvement in model capabilities over training iterations","Publish reproducible results that can be compared across research papers"],"best_for":["ML researchers publishing code generation benchmarks","Teams comparing different model architectures and training approaches","Organizations tracking model improvement over time","Researchers establishing baseline performance on standard benchmarks"],"limitations":["Pass@k assumes k independent samples — expensive to compute for large k values (k=100 requires 97,400 total generations)","Metric is binary (pass/fail) — does not distinguish between solutions that are close to correct vs completely wrong","Does not account for code quality, efficiency, or style — only functional correctness","Requires multiple generations per problem — increases computational cost and latency","Statistical variance increases with k — results become less stable for very large k values"],"requires":["Code generation model capable of producing k samples per problem","Test harness to evaluate each sample against test cases","Aggregation logic to compute pass@k across all problems","Sufficient computational resources to generate k samples for 974 problems"],"input_types":["k generated code samples per problem","test case definitions and expected outputs"],"output_types":["pass@1, pass@10, pass@100 metrics","per-problem pass rates","aggregated statistics and confidence intervals"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mbpp-mostly-basic-python-problems__cap_5","uri":"capability://data.processing.analysis.cross.model.performance.comparison.and.ranking","name":"cross-model performance comparison and ranking","description":"Enables systematic comparison of different code generation models by running them all against the same 974 problems with identical test cases and evaluation criteria. Results are aggregated into leaderboard-style rankings showing pass@k metrics for each model. This standardized comparison framework allows researchers to objectively assess which models perform better on basic programming tasks.","intents":["Compare performance of different code generation models objectively","Rank models by their ability to solve basic programming problems","Identify which models have improved most over time","Understand relative strengths and weaknesses of different architectures"],"best_for":["Researchers publishing model comparisons and benchmarks","Teams evaluating whether to adopt a new code generation model","Organizations tracking progress in code generation capabilities","Communities maintaining leaderboards and benchmark rankings"],"limitations":["Comparison is limited to basic programming problems — does not reflect performance on complex, real-world code","Models may be optimized specifically for MBPP — results may not generalize to other code generation tasks","Comparison does not account for model size, latency, or resource requirements — only raw correctness","Results are snapshot in time — models continue to improve and results become stale","No statistical significance testing — small differences in pass rates may not be meaningful"],"requires":["Multiple code generation models to compare","Standardized evaluation harness that runs all models identically","Aggregation and ranking logic","Sufficient computational resources to evaluate all models"],"input_types":["code generation model outputs for all 974 problems","test case definitions"],"output_types":["ranked list of models by pass@k metrics","comparative performance tables","per-problem performance breakdowns by model"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mbpp-mostly-basic-python-problems__cap_6","uri":"capability://data.processing.analysis.problem.difficulty.and.concept.coverage.analysis","name":"problem difficulty and concept coverage analysis","description":"Analyzes the distribution of problem difficulty, concept coverage, and solution complexity across the 974 problems. Provides insights into what programming concepts are well-represented in the dataset and which are underrepresented. Enables researchers to understand the breadth and balance of the benchmark and identify potential gaps in coverage.","intents":["Understand the scope and coverage of programming concepts in the benchmark","Identify which programming areas are well-tested vs underrepresented","Assess whether the benchmark provides balanced coverage across concepts","Determine if the benchmark is suitable for evaluating specific programming skills"],"best_for":["Researchers designing new benchmarks or extending MBPP","Teams understanding what programming skills their models have learned","Educators using the dataset to understand concept coverage","Organizations assessing whether MBPP is appropriate for their evaluation needs"],"limitations":["Analysis is limited to metadata provided in the dataset — no automatic difficulty estimation","Concept coverage is based on manual tagging — may be incomplete or inconsistent","No analysis of problem interdependencies — some concepts build on others but are not explicitly linked","Difficulty assessment is subjective — different researchers may rate problems differently","No analysis of real-world relevance — problems may not reflect actual programming tasks"],"requires":["Access to problem metadata with concept tags and difficulty ratings","Statistical analysis tools to compute coverage metrics","Visualization tools to display concept distribution"],"input_types":["problem definitions with concept tags and metadata"],"output_types":["concept coverage statistics","difficulty distribution analysis","concept-specific problem counts","coverage gap identification"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mbpp-mostly-basic-python-problems__cap_7","uri":"capability://code.generation.editing.reference.solution.and.test.case.provision","name":"reference solution and test case provision","description":"Includes a correct reference implementation and three test cases for each of the 974 problems, enabling both positive and negative evaluation modes. The reference solutions are hand-written Python functions demonstrating the expected behavior, while test cases cover typical inputs, edge cases, and boundary conditions. This allows evaluation of generated code by comparing outputs to reference solutions or by running test cases directly, supporting both execution-based and semantic-based evaluation approaches.","intents":["Validate generated code by executing it against reference test cases","Compare generated code semantically to reference solutions for style/efficiency analysis","Use reference solutions as few-shot examples in prompts to improve model performance","Analyze how generated code differs from reference implementations (e.g., alternative algorithms)"],"best_for":["Researchers evaluating code generation models on functional correctness","Teams using few-shot prompting to improve code generation quality","Organizations analyzing generated code for efficiency and style"],"limitations":["Reference solutions are single implementations — may not represent all correct approaches or optimal algorithms","Test cases are minimal (3 per problem) — may not cover all edge cases or corner cases","No test case difficulty or coverage metrics — unclear which tests are most important","Reference solutions may have bugs or suboptimal implementations, affecting evaluation validity"],"requires":["Problem metadata with 'code' and 'test_list' fields","Python 3.7+ to execute reference solutions and test cases"],"input_types":["problem ID or description","generated code (as string)"],"output_types":["reference solution (Python function code)","test cases (list of dicts with 'input' and 'output' keys)","pass/fail results for each test case"],"categories":["code-generation-editing","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mbpp-mostly-basic-python-problems__headline","uri":"capability://testing.quality.benchmark.dataset.for.basic.python.programming.problems","name":"benchmark dataset for basic python programming problems","description":"A comprehensive dataset of 974 Python programming problems designed to evaluate basic coding skills, including string manipulation and data structures, making it ideal for assessing foundational programming knowledge.","intents":["best dataset for Python coding problems","Python programming problems for skill assessment","dataset for evaluating basic programming proficiency","Python coding challenges for beginners","benchmark for Python code generation evaluation"],"best_for":["evaluating basic Python skills","training AI models for code generation"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"low","permissions":["Python 3.6+ runtime for executing generated code and test cases","Hugging Face datasets library for loading the benchmark","Code generation model with Python output capability","Test harness to execute generated code and compare against expected outputs","Python 3.6+ with ability to execute arbitrary code","Sandboxing mechanism 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