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| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Extracts 6 million function-docstring pairs from public GitHub repositories across Python, Java, JavaScript, PHP, Ruby, and Go using AST parsing and heuristic matching to align code blocks with their associated natural language documentation. The dataset structures these pairs with metadata (repository, file path, function signature) enabling large-scale supervised training of code understanding models. Implementation uses language-specific parsers to identify function boundaries and docstring conventions (docstrings, JSDoc, Javadoc, etc.) with fuzzy matching to handle inconsistent documentation patterns.
Unique: Combines AST-based function extraction with docstring heuristic matching across 6 languages in a single unified dataset, enabling cross-language code understanding research. The scale (6M pairs) and multi-language coverage was novel at publication (2019) and influenced the architecture of subsequent code models like CodeBERT which used this dataset for pre-training.
vs alternatives: Larger and more diverse than earlier code datasets (e.g., StackOverflow snippets) and includes multiple languages in one benchmark, whereas most prior work focused on single-language datasets or synthetic code-comment pairs.
Provides a standardized evaluation protocol where code search systems are scored on their ability to rank relevant functions highly when given natural language queries. The benchmark includes query-function pairs with relevance labels derived from the original docstring-code alignment, enabling metrics like Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), and recall@k. Evaluation is performed by computing similarity between query embeddings and code embeddings, then ranking functions by score and comparing against ground-truth relevant functions.
Unique: Provides a large-scale (6M function) benchmark with standardized train/test splits and evaluation metrics specifically designed for code search, whereas prior code datasets lacked formal evaluation protocols. The benchmark directly influenced how subsequent code models (CodeBERT, GraphCodeBERT) are evaluated in academic papers.
vs alternatives: More comprehensive and language-diverse than earlier code search benchmarks (e.g., CodeSearchNet's predecessor datasets), and includes explicit relevance judgments rather than relying on proxy signals like code similarity or clone detection.
Implements language-specific AST parsing and heuristic-based extraction to identify function definitions and their associated docstrings across 6 programming languages. For each language, the extraction pipeline uses language-specific conventions: Python (docstrings via triple quotes), Java (Javadoc comments), JavaScript (JSDoc), PHP (PHPDoc), Ruby (YARD/RDoc), and Go (comment blocks). The system handles edge cases like nested functions, decorators, type annotations, and multi-line signatures by leveraging language-specific syntax rules and comment parsing.
Unique: Unified extraction pipeline that handles 6 languages with language-specific docstring conventions (docstrings, Javadoc, JSDoc, PHPDoc, YARD, Go comments) in a single codebase, rather than separate language-specific tools. Uses heuristic-based alignment to match docstrings to functions without requiring explicit AST node linking.
vs alternatives: More scalable than manual annotation and more robust than regex-based extraction because it uses proper AST parsing for function boundaries, reducing false positives and false negatives compared to string-matching approaches.
Provides pre-computed dense vector embeddings for all 6 million functions and associated queries using CodeBERT or similar models, enabling researchers to evaluate new ranking or retrieval strategies without re-embedding the entire dataset. Embeddings are stored in a format optimized for similarity search (e.g., FAISS-compatible vectors), allowing fast nearest-neighbor lookup and ranking without loading the full model. This capability abstracts away the computational cost of embedding generation, making the benchmark accessible to researchers without GPU resources.
Unique: Provides pre-computed embeddings for the entire 6M function dataset using a standard model (CodeBERT), enabling rapid evaluation of retrieval algorithms without re-embedding. This was a novel contribution at the time (2019) because prior code datasets did not include pre-computed embeddings, forcing researchers to train embedding models from scratch.
vs alternatives: Dramatically reduces the barrier to entry for code search research compared to starting from raw code, and enables fair comparison across methods by using a shared embedding space rather than each team using different models.
Provides standardized train/test/validation splits of the 6 million function-docstring pairs with stratification by programming language to ensure balanced representation across languages in each split. The split strategy maintains the distribution of languages (Python, Java, JavaScript, PHP, Ruby, Go) across train/test sets, preventing models from overfitting to language-specific patterns or achieving inflated performance on high-resource languages. Splits are deterministic and reproducible, enabling fair comparison across research papers and implementations.
Unique: Implements language-stratified sampling to ensure balanced representation of all 6 languages in train/test splits, preventing models from overfitting to high-resource languages (Python, Java) at the expense of low-resource languages (Ruby, PHP). This design choice directly influenced how subsequent code datasets (e.g., CodeSearchNet's successors) structure their splits.
vs alternatives: More rigorous than random train/test splits because it ensures language distribution is preserved, enabling fair evaluation of multi-language models and preventing spurious performance gains from language-specific biases.
Includes rich metadata for each function-docstring pair: repository owner, repository name, file path, commit hash, and GitHub URL. This metadata enables researchers to trace extracted functions back to their original source, verify data quality, and analyze code search performance by repository characteristics (e.g., popularity, age, language). The provenance information supports reproducibility and allows researchers to filter or analyze subsets of the dataset based on repository properties (e.g., only functions from popular repositories, or only recent commits).
Unique: Includes full GitHub provenance (owner, repo, path, commit) for every function, enabling researchers to trace back to original source and verify data quality. This level of metadata was uncommon in code datasets at the time (2019) and enables reproducibility and auditing.
vs alternatives: More transparent and auditable than datasets that strip metadata or anonymize sources, and enables researchers to analyze performance by data source characteristics rather than treating the dataset as a monolithic collection.
Applies language-specific normalization rules to code snippets to improve consistency and reduce noise: removing comments (except docstrings), normalizing whitespace, standardizing identifier names, and handling language-specific syntax variations. The normalization is applied consistently across all 6 languages using language-specific rules (e.g., Python indentation, Java access modifiers, JavaScript semicolons), enabling models to focus on semantic patterns rather than syntactic variations. Normalization is optional and can be disabled for use cases requiring original code.
Unique: Applies language-specific normalization rules to code across 6 languages in a unified pipeline, rather than using language-agnostic normalization or no normalization at all. This enables models to learn semantic patterns while reducing syntactic noise, improving generalization across different coding styles.
vs alternatives: More sophisticated than simple whitespace normalization because it uses language-specific rules (e.g., Python indentation, Java access modifiers) to handle language-specific syntax variations, and more practical than no normalization because it reduces noise without losing semantic information.
Provides language-aware tokenization and shared vocabulary for code across 6 programming languages. Tokenization handles language-specific syntax (operators, keywords, delimiters) while creating a unified vocabulary that maps tokens from different languages to shared semantic categories. This enables models to process code from any supported language using a single tokenizer and vocabulary, reducing model complexity and enabling cross-language transfer.
Unique: Provides language-aware tokenization with a unified vocabulary across 6 languages, enabling single-model processing of multi-language code. Uses language-specific syntax rules while maintaining semantic equivalence across languages.
vs alternatives: Offers a single shared vocabulary for 6 languages, whereas alternatives like separate language-specific tokenizers require multiple models or complex language-switching logic.
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 CodeSearchNet 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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