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| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Provides 13,328 curated competitive programming problems sourced from Codeforces, AtCoder, and other platforms, each with complete problem statements, reference solutions in multiple programming languages (C++, Python, Java, etc.), and comprehensive test case suites. The dataset is structured with metadata including problem difficulty calibration (median and 95th percentile solution metrics) and both public and hidden test cases, enabling direct evaluation of code generation models against real-world algorithmic challenges without synthetic problem generation.
Unique: Curated from real competitive programming platforms (Codeforces, AtCoder) with difficulty calibration via median/95th percentile metrics, rather than synthetic or classroom problems. Includes both public and hidden test cases enabling true generalization evaluation, and was specifically constructed to train AlphaCode, making it the largest real-world algorithmic problem corpus for code generation.
vs alternatives: Larger and more algorithmically rigorous than HumanEval or MBPP (which focus on simple utility functions), and more representative of real problem-solving than synthetic benchmarks, while providing standardized difficulty stratification absent from raw Codeforces dumps.
Extracts and normalizes reference solutions across multiple programming languages (C++, Python, Java, JavaScript, Go, Rust, etc.) for each problem, with language-agnostic problem metadata and test case specifications. Solutions are parsed and validated against test cases to ensure correctness, enabling cross-language comparison of algorithmic approaches and language-specific implementation patterns for the same problem.
Unique: Provides solutions in 5+ languages per problem with validation against identical test case suites, enabling direct cross-language comparison. Most code datasets focus on a single language; this enables training models to understand language-agnostic algorithmic reasoning.
vs alternatives: Richer than language-specific datasets (e.g., CodeSearchNet for Python only) because it forces models to learn language-independent problem decomposition, and more realistic than synthetic multilingual datasets because solutions come from real competitive programmers.
Separates test cases into public (visible in problem statement) and hidden (used for final evaluation) categories, enabling evaluation of model generalization beyond memorization of example inputs/outputs. Hidden test cases are designed by problem setters to cover edge cases, boundary conditions, and adversarial inputs that public examples may not expose, allowing measurement of true algorithmic correctness vs. overfitting to visible examples.
Unique: Explicitly separates public and hidden test cases with both included in the dataset, enabling researchers to measure generalization gap between public example performance and true correctness. Most benchmarks (HumanEval, MBPP) use only public test cases; this enables evaluation methodology matching real competitive programming.
vs alternatives: More rigorous than single-test-set benchmarks because it prevents overfitting to visible examples and forces models to learn generalizable algorithmic patterns, matching how competitive programming platforms actually evaluate submissions.
Stratifies problems by difficulty using median and 95th percentile solution runtime metrics from real competitive programmers, enabling selection of problems at specific difficulty levels for targeted training or evaluation. Problems are tagged with difficulty ranges (easy, medium, hard, expert) derived from actual submission statistics rather than subjective classification, allowing researchers to study how model performance scales with problem complexity.
Unique: Uses empirical runtime metrics (median and 95th percentile from real submissions) to calibrate difficulty rather than subjective classification or problem setter ratings. This grounds difficulty in measurable performance data and enables reproducible difficulty-based dataset splits.
vs alternatives: More objective than subjective difficulty labels (e.g., 'hard' vs 'medium') and more granular than binary easy/hard splits, enabling fine-grained curriculum learning studies that other datasets don't support.
Extracts and normalizes problem statements from multiple competitive programming platforms (Codeforces, AtCoder, etc.) into a unified format, including problem description, input/output specifications, constraints, and example inputs/outputs. Handles platform-specific formatting (HTML, Markdown, LaTeX mathematical notation) and converts to consistent structured representation, enabling uniform processing across problems from different sources.
Unique: Normalizes problem statements from multiple competitive programming platforms (Codeforces, AtCoder, etc.) into a unified structured format, handling platform-specific HTML/Markdown formatting and mathematical notation. Most datasets use problems from a single platform; this enables cross-platform aggregation.
vs alternatives: More comprehensive than platform-specific datasets because it handles heterogeneous problem statement formats and enables unified processing, while providing more structured problem representation than raw problem text dumps.
Provides infrastructure for executing generated code against test cases with resource limits (timeout, memory), capturing execution results (pass/fail, runtime, memory usage), and validating output correctness. Supports multiple programming languages and handles I/O redirection, standard output comparison, and floating-point tolerance for numerical problems, enabling automated evaluation of code generation model outputs.
Unique: Provides test case execution framework supporting multiple languages with resource limits and structured result capture, enabling safe evaluation of generated code. The dataset includes test case infrastructure designed for AlphaCode evaluation, not just problem data.
vs alternatives: More complete than raw test case files because it includes execution framework and resource limit handling, enabling end-to-end evaluation without requiring researchers to build custom test runners.
Maintains metadata for each problem including source platform (Codeforces, AtCoder, etc.), problem ID, submission date, problem tags (algorithm type, data structure, etc.), and contest context. This enables filtering and analysis by platform, time period, or problem category, and allows tracing problems back to original sources for additional context or updates.
Unique: Preserves source platform and problem metadata (Codeforces problem ID, AtCoder contest, submission date, problem tags) enabling filtering by platform, time period, and algorithmic category. Most aggregated datasets lose this metadata; preserving it enables platform-specific and temporal analysis.
vs alternatives: More useful for analysis and filtering than datasets that strip metadata, and enables reproducibility by allowing problems to be traced back to original sources.
Enables statistical analysis of the 13,328-problem corpus to understand problem distribution across algorithmic categories, difficulty levels, languages, and platforms. Provides aggregate statistics (e.g., percentage of problems requiring dynamic programming, distribution of problem difficulty, language coverage per problem) enabling researchers to characterize the dataset and identify coverage gaps.
Unique: Provides large-scale corpus of 13,328 problems enabling statistical analysis of problem distribution across algorithms, difficulty, and platforms. Most datasets are smaller or don't provide distribution analysis; this scale enables robust statistical characterization.
vs alternatives: Larger and more diverse than smaller benchmarks (HumanEval: 164 problems, MBPP: 974 problems), enabling more robust statistical analysis and better representation of real problem diversity.
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs CodeContests at 60/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
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