APPS (Automated Programming Progress Standard)
DatasetFree10K coding problems across 3 difficulty levels with test suites.
Capabilities8 decomposed
multi-source coding problem aggregation with standardized test harnesses
Medium confidenceAggregates 10,000 coding problems from four distinct online judge platforms (Codewars, AtCoder, Kattis, Codeforces) into a unified dataset schema with normalized problem descriptions, input/output specifications, and executable test suites. Each problem includes an average of 21 test cases extracted from the original platform's validation infrastructure, enabling consistent evaluation across heterogeneous problem sources with different original formats and difficulty classifications.
Combines problems from four independent online judge platforms with heterogeneous formats into a single normalized schema with consistent test execution semantics, rather than using a single-source benchmark like HumanEval or MBPP
10x larger problem set than HumanEval (10K vs 164 problems) with higher algorithmic complexity and real-world difficulty distribution, making it more representative of production code generation challenges
difficulty-stratified problem categorization and filtering
Medium confidencePartitions the 10,000 problems into three discrete difficulty tiers (introductory: 3,639 problems, interview: 5,000 problems, competition: 1,361 problems) based on source platform difficulty ratings and algorithmic complexity. Enables selective evaluation of code generation models against specific skill levels, allowing researchers to measure performance degradation as problem complexity increases and identify capability gaps at each tier.
Explicitly stratifies problems into three difficulty tiers with substantial size per tier (3.6K, 5K, 1.4K), enabling fine-grained analysis of model performance degradation across skill levels rather than treating all problems as equal difficulty
Unlike HumanEval which lacks difficulty stratification, APPS enables researchers to measure whether models have genuine reasoning or are pattern-matching, by comparing performance across tiers
comprehensive test suite execution and pass-rate evaluation
Medium confidenceProvides executable test suites averaging 21 test cases per problem, sourced directly from original online judge platforms and normalized into a unified execution format. Enables end-to-end evaluation of generated code by running test cases against candidate solutions and computing pass rates (percentage of test cases passed), rather than relying on single-example correctness or syntax validation.
Provides 21 test cases per problem on average (vs single example in HumanEval), enabling rigorous pass-rate evaluation and pass@k metrics that measure robustness across multiple test cases rather than single-shot correctness
Comprehensive test suites catch partial solutions and edge case failures that single-example evaluation would miss, providing more reliable quality signals for code generation systems
natural language to code pipeline evaluation
Medium confidenceStructures problems as natural language descriptions paired with input/output specifications and test suites, enabling end-to-end evaluation of the full code generation pipeline from problem understanding through test validation. Problems are sourced from real online judge platforms where humans have already validated problem clarity, creating a realistic distribution of problem statement quality and ambiguity.
Evaluates the complete pipeline from natural language problem description to working code with comprehensive test validation, rather than isolated code completion or API-call tasks, reflecting real-world coding workflows
More challenging than HumanEval because it requires genuine problem understanding and algorithmic reasoning, not just API knowledge or simple pattern completion
algorithmic reasoning and complexity assessment
Medium confidenceCurates problems that require algorithmic thinking, data structure selection, and computational complexity analysis rather than simple API calls or pattern matching. Problems span domains including dynamic programming, graph algorithms, number theory, and combinatorics, sourced from competitive programming platforms (AtCoder, Codeforces, Kattis) where algorithmic rigor is enforced by time and memory limits.
Explicitly sources problems from competitive programming platforms (AtCoder, Codeforces, Kattis) where algorithmic rigor and time/memory limits enforce genuine complexity requirements, rather than using toy problems that can be solved with naive approaches
Tests genuine algorithmic reasoning rather than API knowledge; problems cannot be solved by simple pattern matching or memorization, requiring models to understand data structures, complexity analysis, and algorithm selection
cross-platform problem normalization and schema unification
Medium confidenceNormalizes problems from four heterogeneous online judge platforms (Codewars, AtCoder, Kattis, Codeforces) with different native formats, input/output conventions, and metadata structures into a unified dataset schema. Handles platform-specific quirks such as different test case formats, input parsing conventions, and output validation rules, enabling consistent evaluation across sources without platform-specific branching logic.
Implements custom extraction and normalization logic for four distinct online judge platforms with different native formats, rather than using a single-source dataset or generic web scraping
Unified schema enables consistent evaluation across diverse problem sources without platform-specific branching, whereas single-source benchmarks (HumanEval, MBPP) lack diversity and may have platform-specific biases
problem metadata extraction and structured annotation
Medium confidenceExtracts and structures metadata from problems including difficulty ratings, source platform, problem tags/categories, input/output constraints, and test case counts. Metadata is normalized across platforms despite different native labeling schemes (e.g., Codewars kyu/dan vs Codeforces rating vs AtCoder color), enabling filtering, stratification, and analysis by problem attributes.
Normalizes metadata across four platforms with different native labeling schemes (Codewars kyu/dan, Codeforces rating, AtCoder color, Kattis difficulty) into a unified difficulty scale, rather than preserving platform-specific labels
Enables cross-platform analysis and filtering that would be impossible with platform-specific metadata, allowing researchers to identify performance patterns independent of source platform
large-scale evaluation dataset for model benchmarking
Medium confidenceProvides a curated, publicly available dataset of 10,000 problems with comprehensive test suites, enabling large-scale evaluation of code generation models without requiring researchers to build their own evaluation infrastructure. Dataset is hosted on Hugging Face and can be loaded via standard dataset libraries, reducing friction for reproducible benchmarking and enabling comparison across research groups.
Publicly available on Hugging Face with standardized dataset loading interface, enabling reproducible benchmarking across research groups without custom infrastructure, rather than proprietary or difficult-to-access benchmarks
10x larger than HumanEval (10K vs 164 problems) with more realistic difficulty distribution and comprehensive test suites, enabling more reliable statistical conclusions about model capabilities
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 APPS (Automated Programming Progress Standard), ranked by overlap. Discovered automatically through the match graph.
MBPP (Mostly Basic Python Problems)
974 basic Python problems complementing HumanEval for code evaluation.
CodeContests
13K competitive programming problems from AlphaCode research.
MBPP+
Enhanced Python coding benchmark with rigorous testing.
Big Code Bench
Comprehensive code benchmark — 1,140 practical tasks with real library usage beyond HumanEval.
SWE Lens
AI-driven tool streamlining recruitment with personalized candidate...
HumanEval
OpenAI's code generation benchmark — 164 Python problems with unit tests, pass@k evaluation.
Best For
- ✓ML researchers evaluating code generation models at scale
- ✓teams building LLM-based coding assistants who need rigorous benchmarking
- ✓organizations comparing multiple code generation systems on identical problem sets
- ✓researchers studying scaling laws of code generation with problem difficulty
- ✓teams building progressive coding tutors that adapt to learner skill level
- ✓organizations benchmarking models on difficulty-matched subsets for fair comparison
- ✓researchers computing pass@1, pass@10, pass@100 metrics for code generation models
- ✓teams building code generation systems that need automated correctness validation
Known Limitations
- ⚠test suites are fixed snapshots from original platforms — no dynamic test generation or adversarial test case synthesis
- ⚠problem descriptions inherit ambiguities and language variations from original sources; no normalization of problem statement clarity
- ⚠test coverage varies by source platform; some problems may have edge cases not represented in the 21-test average
- ⚠no explicit mapping of problem prerequisites or dependency chains — problems are independent without curriculum structure
- ⚠difficulty labels are inherited from source platforms without re-validation; no independent difficulty assessment or inter-rater agreement metrics
- ⚠difficulty distribution is imbalanced (interview tier is 50% of dataset, competition tier is only 13%), skewing aggregate statistics
Requirements
Input / Output
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About
Benchmark of 10,000 coding problems spanning three difficulty levels: introductory (3,639), interview (5,000), and competition (1,361). Problems sourced from Codewars, AtCoder, Kattis, and Codeforces with comprehensive test suites averaging 21 tests per problem. Tests the full pipeline from natural language problem description to working code. More challenging than HumanEval as problems require algorithmic thinking, not just API knowledge. Standard benchmark for evaluating code generation systems.
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