What can APPS (Automated Programming Progress Standard) do?

multi-difficulty benchmark evaluation for code generation models, end-to-end code generation pipeline validation, difficulty-stratified performance analysis, comprehensive test suite curation and aggregation, cross-platform problem sourcing and normalization, large-scale benchmark dataset for model training and evaluation

APPS (Automated Programming Progress Standard)

DatasetFree

10K coding problems across 3 difficulty levels with test suites.

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

multi-difficulty benchmark evaluation for code generation models

Medium confidence

Provides a stratified dataset of 10,000 coding problems across three difficulty tiers (introductory: 3,639, interview: 5,000, competition: 1,361) sourced from production coding platforms (Codewars, AtCoder, Kattis, Codeforces). Enables systematic evaluation of code generation systems across skill levels by measuring end-to-end performance from natural language problem descriptions to executable code, with each problem paired with comprehensive test suites averaging 21 test cases per problem. The stratification allows researchers to isolate model performance degradation as problem complexity increases.

Solves for

Benchmark my code generation model against a standardized, multi-tier difficulty scale to understand where it failsCompare my LLM's performance across introductory, interview, and competition-level algorithmic problemsEvaluate whether my code generation system can handle real-world algorithmic challenges beyond simple API usageMeasure the gap between my model's performance on easy vs. hard problems to identify architectural weaknesses

Best for

ML researchers evaluating code generation models (Codex, GPT-4, Claude, open-source LLMs)

Teams building code synthesis systems who need standardized evaluation beyond HumanEval

Academic groups studying algorithmic reasoning in language models

Requires

Hugging Face Datasets library (datasets>=2.0.0) or direct download capability

Python 3.7+ for dataset loading and processing

Code execution environment (Docker, isolated VM, or sandboxed runtime) to safely run generated code against test suites

Limitations

Problems are primarily algorithmic/competitive programming focused — limited coverage of web development, systems programming, or domain-specific code patterns

Test suites are deterministic and may not catch all edge cases or performance regressions in generated code

No built-in evaluation harness — requires custom infrastructure to run tests, parse outputs, and aggregate metrics

What makes it unique

Stratified difficulty sampling (3,639 intro / 5,000 interview / 1,361 competition) sourced from four production competitive programming platforms with comprehensive test suites (avg 21 tests/problem), enabling fine-grained analysis of model degradation across skill levels — more rigorous than HumanEval's single-difficulty, API-focused problems

vs alternatives

More challenging and comprehensive than HumanEval (164 problems, single difficulty) because it requires algorithmic reasoning across three tiers and includes real-world test suites from competitive programming platforms rather than synthetic API-call problems

end-to-end code generation pipeline validation

Medium confidence

Validates the complete pipeline from natural language problem specification to working executable code by requiring generated solutions to pass comprehensive test suites. Each problem includes the problem statement (natural language description), input/output specifications, and 21 test cases on average that cover normal cases, edge cases, and boundary conditions. The dataset structure enforces that models must perform full semantic understanding, algorithmic reasoning, and code synthesis in a single pass without intermediate feedback loops.

Solves for

Test whether my code generation model can produce fully working solutions without human intervention or iterative refinementMeasure end-to-end success rate (code that passes all tests) rather than just syntactic correctnessIdentify failure modes where my model generates syntactically valid but semantically incorrect codeValidate that my model understands problem constraints and edge cases, not just happy-path scenarios

Best for

Code generation model developers who need strict pass/fail evaluation criteria

Teams building autonomous code synthesis tools that must work without human review

Researchers studying the gap between syntactic correctness and semantic correctness in LLM outputs

Requires

Code execution sandbox with timeout/resource limits to safely run untrusted generated code

Test harness that can parse problem specifications, execute code with test inputs, and compare outputs

Python 3.7+ or language-specific runtime for executing generated code

Limitations

Test suites are finite and deterministic — generated code may pass all tests but fail on unseen inputs or have subtle bugs

No evaluation of code efficiency — a solution passing tests in O(n²) time counts equally to O(n log n), missing performance regressions

Requires exact output matching (or custom comparison logic) — floating-point precision, whitespace, or output formatting differences cause false failures

What makes it unique

Enforces full pipeline validation with comprehensive test suites (avg 21 tests per problem) that cover edge cases and boundary conditions, not just happy-path scenarios — requires models to demonstrate semantic correctness, not just syntactic validity or partial understanding

vs alternatives

More rigorous than simple code-completion benchmarks because it requires generated code to pass all test cases, catching semantic errors and edge-case failures that syntax-only validation would miss

difficulty-stratified performance analysis

Medium confidence

Enables comparative analysis of code generation model performance across three discrete difficulty tiers by partitioning the 10,000 problems into introductory (3,639), interview (5,000), and competition (1,361) subsets. Each tier represents increasing algorithmic complexity, allowing researchers to measure performance degradation curves and identify the difficulty threshold where models begin to fail. The stratification is sourced from the original platform classifications (Codewars, AtCoder, Kattis, Codeforces), ensuring consistency with industry-standard problem difficulty ratings.

Solves for

Measure at what difficulty level my code generation model's performance drops significantlyCompare two models' relative strengths on easy vs. hard problems to understand their architectural differencesIdentify whether my model has a sharp cliff or gradual degradation as problem complexity increasesEvaluate whether my model is suitable for introductory coding tasks vs. interview-level algorithmic challenges

Best for

Model researchers studying scaling laws and capability emergence across difficulty levels

Teams selecting code generation models for specific use cases (e.g., beginner coding tutors vs. senior engineer tools)

Academic studies on the relationship between model size/training data and algorithmic reasoning ability

Requires

Hugging Face Datasets library to load and filter problems by difficulty tier

Python 3.7+ for data manipulation and statistical analysis

Evaluation harness capable of running tests across all three tiers independently

Limitations

Difficulty ratings are platform-specific and may not align perfectly across Codewars, AtCoder, Kattis, and Codeforces — some problems may be miscategorized

Introductory tier is smaller (3,639) than interview tier (5,000), creating statistical imbalance for comparative analysis

No fine-grained difficulty scoring within tiers — all 'interview' problems treated equally despite potential 2-3x complexity variation

What makes it unique

Provides three discrete, platform-validated difficulty tiers (introductory/interview/competition) with substantial problem counts per tier (3,639/5,000/1,361), enabling statistically meaningful performance degradation analysis across skill levels — most benchmarks lack this stratification or use arbitrary difficulty scoring

vs alternatives

Enables difficulty-stratified analysis that HumanEval cannot provide (single difficulty level), allowing researchers to identify the exact capability ceiling of their models rather than just a single aggregate score

comprehensive test suite curation and aggregation

Medium confidence

Aggregates test suites from four production competitive programming platforms (Codewars, AtCoder, Kattis, Codeforces) with an average of 21 test cases per problem, covering normal cases, edge cases, boundary conditions, and performance constraints. Test cases are sourced from platform-validated problem sets where human competitors have solved problems, ensuring test quality and coverage. The dataset preserves the original test structure and specifications, allowing evaluation systems to run tests in isolated environments with timeout and resource constraints.

Solves for

Evaluate my code generation model against real-world test suites, not synthetic or hand-crafted test casesEnsure my evaluation captures edge cases and boundary conditions that simple test cases might missMeasure whether generated code handles input parsing, output formatting, and constraint satisfaction correctlyValidate that my code generation system produces solutions robust enough for production use

Best for

Researchers building code generation evaluation frameworks who need high-quality, diverse test suites

Teams benchmarking code generation models and need to ensure test quality is not a confounding variable

Organizations evaluating code generation for use cases where test coverage is critical (e.g., automated code review)

Requires

Hugging Face Datasets library to load test case data

Python 3.7+ for parsing and executing test cases

Code execution sandbox with I/O redirection to capture and compare test outputs

Limitations

Test suites are platform-specific and may reflect biases in problem design (e.g., Codeforces emphasizes algorithmic efficiency, AtCoder emphasizes correctness)

Average of 21 tests per problem may be insufficient for complex problems with many edge cases — some problems may have inadequate coverage

Test cases are deterministic and do not cover non-deterministic behavior, concurrency, or randomized algorithms

What makes it unique

Aggregates test suites from four production competitive programming platforms with platform-validated problem sets and average 21 tests per problem, ensuring test quality is derived from real human-solved problems rather than synthetic or hand-crafted test cases

vs alternatives

More comprehensive and realistic than synthetic test suites because tests are sourced from actual competitive programming platforms where human competitors have validated problem correctness and test coverage

cross-platform problem sourcing and normalization

Medium confidence

Aggregates 10,000 coding problems from four distinct competitive programming platforms (Codewars, AtCoder, Kattis, Codeforces) and normalizes them into a unified dataset format. Each problem is extracted with its natural language description, input/output specifications, constraints, and associated test cases, then standardized to enable consistent evaluation across platform-specific variations in problem statement style, I/O format, and constraint specification. The normalization process preserves problem semantics while enabling unified evaluation infrastructure.

Solves for

Access a diverse set of coding problems from multiple authoritative sources without building custom scrapers for each platformEvaluate my code generation model on problems from different platforms to ensure it generalizes across problem statement stylesAvoid platform-specific biases by training or evaluating on a balanced mix of problems from Codewars, AtCoder, Kattis, and CodeforcesLeverage platform-specific problem quality (e.g., Codeforces for algorithmic rigor, Codewars for beginner-friendly problems)

Best for

Researchers building code generation benchmarks who need diverse, high-quality problem sources

Teams evaluating code generation models and want to ensure evaluation is not biased toward a single platform's problem style

Organizations studying how code generation models generalize across different problem statement formats and constraint specifications

Requires

Hugging Face Datasets library to access the pre-aggregated dataset

Python 3.7+ for data processing and analysis

Understanding of platform-specific problem formats (Codeforces, AtCoder, Kattis, Codewars) to interpret normalized data correctly

Limitations

Normalization may lose platform-specific nuances or problem context that could affect solution strategy

Problem distribution across platforms is uneven (Codeforces: 5,000 interview problems, others smaller), potentially skewing evaluation toward Codeforces-style problems

Platform-specific constraints (e.g., time limits, memory limits) may not be fully captured or standardized in the dataset

What makes it unique

Aggregates and normalizes problems from four distinct competitive programming platforms (Codewars, AtCoder, Kattis, Codeforces) into a unified format, preserving platform diversity while enabling consistent evaluation — most benchmarks source from a single platform or use synthetic problems

vs alternatives

Provides platform diversity that single-source benchmarks lack, reducing evaluation bias and enabling analysis of how code generation models generalize across different problem statement styles and constraint specifications

large-scale benchmark dataset for model training and evaluation

Medium confidence

Provides a dataset of 10,000 coding problems suitable for both training code generation models (via supervised fine-tuning on problem-solution pairs) and evaluating model performance at scale. The dataset size and diversity enable statistical significance in model comparisons and support training of specialized code generation models. Problems span three difficulty levels and multiple algorithmic domains, providing sufficient variety to avoid overfitting to specific problem patterns.

Solves for

Fine-tune my code generation model on a large, diverse dataset of real coding problemsEvaluate my model on a statistically significant benchmark (10,000 problems) to ensure results are not due to random chanceCompare multiple code generation models on the same large-scale benchmark to identify the best performerStudy how model performance scales with training data size by using subsets of the APPS dataset

Best for

ML researchers training specialized code generation models (e.g., fine-tuning CodeBERT, GPT-2, or other base models)

Teams benchmarking code generation models and need sufficient problem count for statistical significance

Academic groups studying scaling laws in code generation (model size vs. performance on APPS)

Requires

Hugging Face Datasets library (datasets>=2.0.0) for loading and processing

Python 3.7+ for data manipulation

GPU or TPU for efficient model training (recommended for fine-tuning large models)

Limitations

10,000 problems may be insufficient for training very large models (100B+ parameters) without data augmentation or repetition

Dataset is static — no mechanism for continuous updates as new problems are added to source platforms

Imbalanced difficulty distribution (5,000 interview vs. 1,361 competition) may bias model training toward interview-level problems

What makes it unique

Provides 10,000 problems across three difficulty tiers with comprehensive test suites, enabling both supervised fine-tuning of code generation models and large-scale evaluation with statistical significance — most code generation datasets are either smaller (HumanEval: 164 problems) or lack test suites for rigorous evaluation

vs alternatives

Larger and more comprehensive than HumanEval (164 problems) and includes test suites for rigorous evaluation, making it suitable for both training and benchmarking code generation models at production scale

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.

Benchmark21

bigcode-models-leaderboard

bigcode-models-leaderboard — AI demo on HuggingFace

automated code generation model benchmarking with standardized evaluation metricsmulti-language code generation task evaluation

2 shared capabilities

Benchmark42

LiveCodeBench

Continuously updated coding benchmark — new competitive programming problems, prevents contamination.

multi-scenario code capability evaluationproblem difficulty stratification and easy subset evaluation

2 shared capabilities

Dataset48

MBPP (Mostly Basic Python Problems)

974 basic Python problems complementing HumanEval for code evaluation.

multi-model comparative evaluation frameworkpython code generation benchmark evaluation

2 shared capabilities

Dataset48

CodeContests

13K competitive programming problems from AlphaCode research.

test-case-driven-code-evaluation-harnessdifficulty-calibrated-problem-stratification

2 shared capabilities

Dataset45

xCodeEval

Multilingual code evaluation across 17 languages.

multilingual code generation benchmarking across 17 languages with execution-based validationthree-phase evaluation pipeline with generation, execution, and metrics computation

2 shared capabilities

Repository44

CodeT5

Home of CodeT5: Open Code LLMs for Code Understanding and Generation

humaneval benchmark evaluation with pass@k metrics

1 shared capability

Best For

✓ML researchers evaluating code generation models (Codex, GPT-4, Claude, open-source LLMs)
✓Teams building code synthesis systems who need standardized evaluation beyond HumanEval
✓Academic groups studying algorithmic reasoning in language models
✓Companies benchmarking internal code generation tools against public standards
✓Code generation model developers who need strict pass/fail evaluation criteria
✓Teams building autonomous code synthesis tools that must work without human review
✓Researchers studying the gap between syntactic correctness and semantic correctness in LLM outputs
✓Organizations evaluating whether code generation is production-ready for their use case

Known Limitations

⚠Problems are primarily algorithmic/competitive programming focused — limited coverage of web development, systems programming, or domain-specific code patterns
⚠Test suites are deterministic and may not catch all edge cases or performance regressions in generated code
⚠No built-in evaluation harness — requires custom infrastructure to run tests, parse outputs, and aggregate metrics
⚠Language coverage skewed toward Python (majority of problems) with limited multi-language problem variants
⚠Static test cases cannot evaluate code quality attributes like readability, maintainability, or security vulnerabilities
⚠Test suites are finite and deterministic — generated code may pass all tests but fail on unseen inputs or have subtle bugs

Requirements

Hugging Face Datasets library (datasets>=2.0.0) or direct download capabilityPython 3.7+ for dataset loading and processingCode execution environment (Docker, isolated VM, or sandboxed runtime) to safely run generated code against test suitesCustom evaluation script to parse problem descriptions, execute generated code, and compare outputs against expected resultsCode execution sandbox with timeout/resource limits to safely run untrusted generated codeTest harness that can parse problem specifications, execute code with test inputs, and compare outputsPython 3.7+ or language-specific runtime for executing generated codeMemory and CPU constraints to prevent infinite loops or resource exhaustion during test execution

Input / Output

Accepts: natural language problem descriptions (English text), structured problem metadata (difficulty level, source platform, problem ID), generated code (Python, Java, C++, JavaScript, or other languages), natural language problem description (English text), input/output format specifications (text description or structured schema), generated source code (Python, Java, C++, JavaScript, etc.), problem metadata including difficulty tier classification (string: 'introductory', 'interview', 'competition'), generated code solutions for problems across all three tiers, test case specifications (input data, expected output, constraints), generated code to be tested (Python, Java, C++, JavaScript, etc.), problem metadata from four competitive programming platforms (Codewars, AtCoder, Kattis, Codeforces), problem statements (natural language descriptions), input/output specifications and constraints, problem descriptions (natural language text), solution code (Python, Java, C++, JavaScript, etc.), problem metadata (difficulty, source platform, problem ID)

Produces: pass/fail test results per problem, aggregate metrics (accuracy rate, difficulty-stratified performance), structured evaluation reports (JSON/CSV with per-problem results), boolean pass/fail per test case, aggregate pass rate (number of tests passed / total tests), error messages or execution logs (timeout, runtime error, assertion failure), per-tier accuracy metrics (pass rate for each difficulty level), performance degradation curves (accuracy vs. difficulty), comparative analysis reports (model A vs. model B across tiers), test execution results (pass/fail per test case), execution logs (stdout, stderr, runtime, memory usage), aggregate test coverage metrics (tests passed / total tests), normalized problem dataset (JSON or structured format), unified problem representation with platform source metadata, standardized test case format, training data formatted as (input, target) pairs for supervised fine-tuning, evaluation metrics (accuracy, pass rate, difficulty-stratified performance), model checkpoints and training logs

UnfragileRank

Adoption70%(35% weight)

Quality28%(25% weight)

Ecosystem50%(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

6 capabilities

Visit APPS (Automated Programming Progress Standard)→

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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Capabilities6 decomposed

multi-difficulty benchmark evaluation for code generation models

Medium confidence

Solves for

Best for

ML researchers evaluating code generation models (Codex, GPT-4, Claude, open-source LLMs)

Teams building code synthesis systems who need standardized evaluation beyond HumanEval

Academic groups studying algorithmic reasoning in language models

Requires

Hugging Face Datasets library (datasets>=2.0.0) or direct download capability

Python 3.7+ for dataset loading and processing

Code execution environment (Docker, isolated VM, or sandboxed runtime) to safely run generated code against test suites

Limitations

Problems are primarily algorithmic/competitive programming focused — limited coverage of web development, systems programming, or domain-specific code patterns

Test suites are deterministic and may not catch all edge cases or performance regressions in generated code

No built-in evaluation harness — requires custom infrastructure to run tests, parse outputs, and aggregate metrics

What makes it unique

vs alternatives

end-to-end code generation pipeline validation

Medium confidence

Solves for

Best for

Code generation model developers who need strict pass/fail evaluation criteria

Teams building autonomous code synthesis tools that must work without human review

Researchers studying the gap between syntactic correctness and semantic correctness in LLM outputs

Requires

Code execution sandbox with timeout/resource limits to safely run untrusted generated code

Test harness that can parse problem specifications, execute code with test inputs, and compare outputs

Python 3.7+ or language-specific runtime for executing generated code

Limitations

Test suites are finite and deterministic — generated code may pass all tests but fail on unseen inputs or have subtle bugs

No evaluation of code efficiency — a solution passing tests in O(n²) time counts equally to O(n log n), missing performance regressions

Requires exact output matching (or custom comparison logic) — floating-point precision, whitespace, or output formatting differences cause false failures

What makes it unique

vs alternatives

More rigorous than simple code-completion benchmarks because it requires generated code to pass all test cases, catching semantic errors and edge-case failures that syntax-only validation would miss

difficulty-stratified performance analysis

Medium confidence

Solves for

Best for

Model researchers studying scaling laws and capability emergence across difficulty levels

Teams selecting code generation models for specific use cases (e.g., beginner coding tutors vs. senior engineer tools)

Academic studies on the relationship between model size/training data and algorithmic reasoning ability

Requires

Hugging Face Datasets library to load and filter problems by difficulty tier

Python 3.7+ for data manipulation and statistical analysis

Evaluation harness capable of running tests across all three tiers independently

Limitations

Difficulty ratings are platform-specific and may not align perfectly across Codewars, AtCoder, Kattis, and Codeforces — some problems may be miscategorized

Introductory tier is smaller (3,639) than interview tier (5,000), creating statistical imbalance for comparative analysis

No fine-grained difficulty scoring within tiers — all 'interview' problems treated equally despite potential 2-3x complexity variation

What makes it unique

vs alternatives

comprehensive test suite curation and aggregation

Medium confidence

Solves for

Best for

Researchers building code generation evaluation frameworks who need high-quality, diverse test suites

Teams benchmarking code generation models and need to ensure test quality is not a confounding variable

Organizations evaluating code generation for use cases where test coverage is critical (e.g., automated code review)

Requires

Hugging Face Datasets library to load test case data

Python 3.7+ for parsing and executing test cases

Code execution sandbox with I/O redirection to capture and compare test outputs

Limitations

Test suites are platform-specific and may reflect biases in problem design (e.g., Codeforces emphasizes algorithmic efficiency, AtCoder emphasizes correctness)

Average of 21 tests per problem may be insufficient for complex problems with many edge cases — some problems may have inadequate coverage

Test cases are deterministic and do not cover non-deterministic behavior, concurrency, or randomized algorithms

What makes it unique

vs alternatives

cross-platform problem sourcing and normalization

Medium confidence

Solves for

Best for

Researchers building code generation benchmarks who need diverse, high-quality problem sources

Teams evaluating code generation models and want to ensure evaluation is not biased toward a single platform's problem style

Organizations studying how code generation models generalize across different problem statement formats and constraint specifications

Requires

Hugging Face Datasets library to access the pre-aggregated dataset

Python 3.7+ for data processing and analysis

Understanding of platform-specific problem formats (Codeforces, AtCoder, Kattis, Codewars) to interpret normalized data correctly

Limitations

Normalization may lose platform-specific nuances or problem context that could affect solution strategy

Problem distribution across platforms is uneven (Codeforces: 5,000 interview problems, others smaller), potentially skewing evaluation toward Codeforces-style problems

Platform-specific constraints (e.g., time limits, memory limits) may not be fully captured or standardized in the dataset

What makes it unique

vs alternatives

large-scale benchmark dataset for model training and evaluation

Medium confidence

Solves for

Best for

ML researchers training specialized code generation models (e.g., fine-tuning CodeBERT, GPT-2, or other base models)

Teams benchmarking code generation models and need sufficient problem count for statistical significance

Academic groups studying scaling laws in code generation (model size vs. performance on APPS)

Requires

Hugging Face Datasets library (datasets>=2.0.0) for loading and processing

Python 3.7+ for data manipulation

GPU or TPU for efficient model training (recommended for fine-tuning large models)

Limitations

10,000 problems may be insufficient for training very large models (100B+ parameters) without data augmentation or repetition

Dataset is static — no mechanism for continuous updates as new problems are added to source platforms

Imbalanced difficulty distribution (5,000 interview vs. 1,361 competition) may bias model training toward interview-level problems

What makes it unique

vs alternatives

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

About

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APPS (Automated Programming Progress Standard)

Capabilities6 decomposed

multi-difficulty benchmark evaluation for code generation models

end-to-end code generation pipeline validation

difficulty-stratified performance analysis

comprehensive test suite curation and aggregation

cross-platform problem sourcing and normalization

large-scale benchmark dataset for model training and evaluation

Related Artifactssharing capabilities

bigcode-models-leaderboard

LiveCodeBench

MBPP (Mostly Basic Python Problems)

CodeContests

xCodeEval

CodeT5

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to APPS (Automated Programming Progress Standard)

Are you the builder of APPS (Automated Programming Progress Standard)?

Get the weekly brief

Data Sources

APPS (Automated Programming Progress Standard)

Capabilities6 decomposed

multi-difficulty benchmark evaluation for code generation models

end-to-end code generation pipeline validation

difficulty-stratified performance analysis

comprehensive test suite curation and aggregation

cross-platform problem sourcing and normalization

large-scale benchmark dataset for model training and evaluation

Related Artifactssharing capabilities

bigcode-models-leaderboard

LiveCodeBench

MBPP (Mostly Basic Python Problems)

CodeContests

xCodeEval

CodeT5

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to APPS (Automated Programming Progress Standard)

Are you the builder of APPS (Automated Programming Progress Standard)?

Get the weekly brief

Data Sources