What can xCodeEval do?

multilingual code generation benchmarking across 17 languages with execution-based validation, src_uid-based cross-task data linking and problem deduplication, pass@k metric computation for multi-sample code generation evaluation, multilingual problem description and unit test corpus with 7,500 unique problems, three-phase evaluation pipeline with generation, execution, and metrics computation, code translation evaluation with language-pair specific test execution, automatic program repair (apr) evaluation with test-driven validation, natural language to code retrieval with semantic matching, code-to-code retrieval with semantic similarity matching, tag classification for code understanding and categorization, code compilation validation across 17 languages with compiler-specific error handling, hugging face datasets api integration with automatic src_uid resolution, execeval containerized execution engine with language-specific runtime configuration

xCodeEval

DatasetFree

Multilingual code evaluation across 17 languages.

Open Source

/ 100

13 capabilities

Capabilities13 decomposed

multilingual code generation benchmarking across 17 languages with execution-based validation

Medium confidence

Provides a standardized evaluation framework for code generation models that spans 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) using an execution-based metric system rather than string matching. The ExecEval engine compiles and runs generated code against unit test suites stored in unittest_db.json, measuring pass@k rates to determine functional correctness across language implementations of the same problem.

Solves for

Evaluate code generation models on their ability to produce functionally correct code across multiple programming languagesBenchmark multilingual LLM performance using execution-based metrics rather than BLEU or token-level similarityCompare how well models generalize code generation across languages with different syntax and runtime characteristicsMeasure pass@k performance to understand model reliability when sampling multiple generations per problem

Best for

ML researchers evaluating multilingual code generation models

Teams building cross-language code synthesis systems

Organizations benchmarking LLM performance on functional correctness rather than syntactic similarity

Requires

Python 3.7+

Docker (latest) for ExecEval execution engine

Hugging Face datasets library or Git LFS 2.0+ for data access

Limitations

ExecEval execution engine requires Docker containerization — cannot evaluate code without Docker runtime

Evaluation latency scales with number of test cases and language compilation times; no built-in caching of compilation artifacts

Limited to 17 languages; adding new languages requires compiler configuration and unittest_db extension

What makes it unique

Uses execution-based validation with containerized ExecEval engine across 17 languages instead of string-matching metrics; centralizes problem definitions via src_uid linking system to avoid data duplication and enable consistent evaluation across 7 distinct tasks (synthesis, translation, repair, classification, compilation, NL-retrieval, code-retrieval)

vs alternatives

Provides execution-based correctness measurement across more languages than HumanEval (Python-only) and with unified infrastructure for code translation and retrieval tasks, not just generation

src_uid-based cross-task data linking and problem deduplication

Medium confidence

Implements a foreign-key linking system where all 7 task datasets (program synthesis, code translation, APR, tag classification, compilation, NL-code retrieval, code-code retrieval) reference centralized problem definitions and unit tests via unique src_uid identifiers. This architecture eliminates data duplication across 25 million training examples by storing problem descriptions once in problem_descriptions.jsonl and unit tests once in unittest_db.json, with task-specific datasets containing only src_uid pointers and task-specific fields. The Hugging Face datasets API automatically resolves these links during loading.

Solves for

Load task datasets with automatic resolution of linked problem descriptions and unit tests without manual joiningEnsure consistency across tasks by guaranteeing all tasks reference identical problem definitions and test suitesReduce storage footprint by storing problem metadata once instead of duplicating across 7 task datasetsEnable cross-task analysis by tracing which problems appear in multiple tasks via src_uid

Best for

Researchers analyzing how models perform across multiple tasks on the same problems

Teams building multi-task training pipelines that need consistent problem definitions

Data engineers optimizing storage and bandwidth for large-scale dataset distribution

Requires

Hugging Face datasets library (recommended) OR Git LFS 2.0+ for manual access

Python 3.7+ for programmatic linking

Access to NTU-NLP-sg/xCodeEval Hugging Face repository

Limitations

Manual data access via Git LFS requires explicit src_uid linking logic; automatic linking only available through Hugging Face API

Circular dependencies possible if problem_descriptions.jsonl or unittest_db.json are corrupted; no built-in validation of referential integrity

src_uid format and collision handling not documented; potential issues if src_uid generation changes between dataset versions

What makes it unique

Uses src_uid foreign-key system to link 7 heterogeneous task datasets to centralized problem and test definitions, enabling single-source-of-truth problem metadata across 25M examples; Hugging Face API integration automatically resolves links during dataset loading without manual join operations

vs alternatives

Reduces storage overhead compared to task-specific datasets that duplicate problem descriptions; enables consistent evaluation across tasks by guaranteeing identical problem definitions and test suites

pass@k metric computation for multi-sample code generation evaluation

Medium confidence

Computes pass@k metrics by sampling k code generations per problem, executing each sample against unit tests, and measuring the fraction of problems where at least one sample passes all tests. The metric accounts for sampling variance and provides statistical estimates of model reliability when generating multiple candidates. Evaluation pipeline generates k samples per problem (Phase 1), executes all samples (Phase 2), and computes pass@k by checking if any sample produces PASS outcome for all test cases.

Solves for

Measure code generation model reliability when sampling multiple candidates per problemCompare models on their ability to generate at least one correct solution among k attemptsAssess sampling efficiency (how many samples needed to achieve target pass rate)Provide statistically meaningful evaluation metrics that account for sampling variance

Best for

Researchers evaluating code generation models with multiple samples

Teams comparing models on pass@k metrics (standard in code generation benchmarks)

Organizations assessing model reliability and robustness

Requires

Code generation model that can sample k independent generations per problem

ExecEval execution engine for running generated code

Unit tests for each problem

Limitations

Computational cost scales linearly with k; large k values require significant execution time

Pass@k assumes independence between samples; correlated errors may inflate pass@k estimates

Metric does not measure code quality or efficiency; only binary pass/fail per problem

What makes it unique

Integrates pass@k computation into unified evaluation pipeline alongside execution outcomes; supports pass@k for all 7 tasks (synthesis, translation, APR, etc.), not just code generation

vs alternatives

Standard metric in code generation benchmarks; accounts for sampling variance; enables fair comparison across models with different sampling strategies

multilingual problem description and unit test corpus with 7,500 unique problems

Medium confidence

Provides centralized repository of 7,500 unique programming problems with natural language descriptions and language-agnostic unit test specifications stored in problem_descriptions.jsonl and unittest_db.json. Each problem is linked to multiple code implementations across the 17 supported languages via src_uid, enabling consistent evaluation across tasks. Problem descriptions include problem statement, input/output specifications, and constraints; unit tests include test cases with expected outputs that apply to all language implementations.

Solves for

Access standardized problem definitions for training and evaluating code modelsRetrieve unit tests that apply consistently across all 17 languagesAnalyze problem characteristics (difficulty, algorithm type, domain) across the corpusUse problems as basis for multi-task evaluation (synthesis, translation, retrieval, etc.)

Best for

Researchers building code generation and understanding models

Teams creating multilingual training datasets

Organizations benchmarking code models across diverse problem types

Requires

Access to problem_descriptions.jsonl and unittest_db.json files

Hugging Face datasets library or Git LFS for data access

Limitations

Problem descriptions are in English only; no multilingual problem statements

Unit tests must be language-agnostic (I/O-based); domain-specific tests not supported

Problem difficulty and algorithm type not explicitly labeled; must be inferred from description

What makes it unique

Provides 7,500 problems with consistent unit tests across 17 languages; centralized storage via src_uid linking eliminates duplication and ensures consistency across 7 tasks and 25M training examples

vs alternatives

Larger and more diverse than HumanEval (164 problems); supports more languages and tasks; consistent test suites across languages enable fair cross-language evaluation

three-phase evaluation pipeline with generation, execution, and metrics computation

Medium confidence

Implements standardized evaluation workflow with three distinct phases: Phase 1 (Generation) accepts code generation models and produces k samples per problem; Phase 2 (Execution) runs samples through ExecEval to obtain execution outcomes; Phase 3 (Metrics) computes pass@k and task-specific metrics from execution results. This separation of concerns enables modular evaluation, supports different generation strategies (beam search, sampling, etc.), and provides intermediate results for debugging and analysis.

Solves for

Evaluate code generation models using standardized three-phase pipelineGenerate multiple samples per problem and measure pass@k metricsCapture intermediate results (generated code, execution outcomes) for analysisSupport different generation strategies without modifying evaluation infrastructure

Best for

Researchers evaluating code generation models with standardized pipeline

Teams building evaluation infrastructure for code models

Organizations benchmarking multiple models with consistent methodology

Requires

Code generation model for Phase 1

ExecEval execution engine for Phase 2

Evaluation script to compute metrics in Phase 3

Limitations

Phase separation adds complexity; requires careful coordination between phases

Intermediate results must be stored; large-scale evaluation requires significant disk space

No built-in support for streaming evaluation; all results must be materialized

What makes it unique

Separates generation, execution, and metrics computation into distinct phases; enables modular evaluation and supports different generation strategies without pipeline modification

vs alternatives

Modular design enables reuse of phases for different tasks; intermediate results support debugging and analysis; standardized pipeline ensures consistent evaluation across models

code translation evaluation with language-pair specific test execution

Medium confidence

Evaluates code translation models by executing translated code against the original problem's unit tests, measuring whether translations preserve functional correctness across language pairs. The system stores source code in one language and target code in another, both linked to the same problem definition and test suite via src_uid. ExecEval compiles and runs translated code in the target language runtime, comparing execution outcomes (PASS, RUNTIME_ERROR, COMPILATION_ERROR, TIMEOUT) to determine translation quality beyond syntactic correctness.

Solves for

Measure code translation model accuracy by testing whether translated code produces correct outputsIdentify language-pair specific translation challenges (e.g., Python-to-Rust memory safety issues)Benchmark translation models on functional equivalence rather than string similarity to source codeEvaluate cross-language idiom preservation and runtime behavior consistency

Best for

Researchers building code translation models across multiple language pairs

Teams evaluating automated code migration tools (e.g., Python to Go)

Organizations assessing code modernization pipelines with execution-based validation

Requires

Docker with compilers/runtimes for both source and target languages

ExecEval configuration mapping language pairs to appropriate compilation and execution commands

Unit tests compatible with both source and target language I/O conventions

Limitations

Test suite must be language-agnostic (e.g., I/O-based tests) to apply to both source and target languages; domain-specific tests may not translate

Compilation time varies significantly across languages (Rust slower than Python); evaluation latency not uniform across language pairs

Runtime behavior differences (e.g., integer overflow handling) may cause false negatives if tests assume source language semantics

What makes it unique

Evaluates translation correctness via execution against shared unit tests rather than string matching to source code; supports all 17 languages with language-pair specific compiler/runtime configuration in ExecEval, enabling evaluation of any source-target language combination

vs alternatives

Provides functional correctness measurement for code translation instead of BLEU/token similarity; execution-based approach catches semantic errors that string matching would miss (e.g., off-by-one bugs, type mismatches)

automatic program repair (apr) evaluation with test-driven validation

Medium confidence

Benchmarks APR models by providing buggy code and unit tests, measuring whether repaired code passes all test cases. The system stores buggy code variants linked to problem definitions and test suites via src_uid, allowing ExecEval to execute repaired code and measure pass@k rates. APR generation phase accepts buggy code as input, repair models generate fixed versions, and execution phase validates repairs against the original unit test suite to determine repair accuracy.

Solves for

Evaluate APR models on their ability to fix real bugs while preserving program functionalityMeasure repair success rates across different bug types and programming languagesBenchmark whether repairs are minimal (preserve original code structure) vs. complete rewritesCompare APR model performance across the 17 supported languages

Best for

Researchers developing automated program repair techniques

Teams building code quality tools that suggest fixes for failing tests

Organizations evaluating AI-assisted debugging and code fixing systems

Requires

Buggy code samples linked to problem definitions via src_uid

Comprehensive unit test suites that fail on buggy code

Docker with ExecEval for execution-based validation

Limitations

Buggy code variants must be pre-generated or manually curated; no automatic bug injection mechanism provided

Test suite must be comprehensive enough to catch the injected bugs; weak tests may accept incorrect repairs

Repair evaluation cannot distinguish between minimal fixes and complete rewrites; no code similarity metrics included

What makes it unique

Provides APR evaluation infrastructure with execution-based validation across 17 languages using shared problem definitions and test suites; integrates APR as one of 7 tasks in unified benchmark rather than standalone evaluation framework

vs alternatives

Enables cross-language APR evaluation with consistent test suites; execution-based approach ensures repairs are functionally correct, not just syntactically plausible

natural language to code retrieval with semantic matching

Medium confidence

Enables evaluation of NL-to-code retrieval models by providing natural language problem descriptions and a corpus of code implementations, measuring whether models retrieve correct code solutions. The system stores problem descriptions in problem_descriptions.jsonl and code implementations in a retrieval corpus, both linked via src_uid. Evaluation measures retrieval accuracy (recall@k, MRR) by checking if correct code implementations appear in the top-k retrieved results for each problem description.

Solves for

Evaluate code search and retrieval models on their ability to find relevant code implementations from natural language queriesMeasure semantic matching quality between problem descriptions and code solutionsBenchmark retrieval models on recall@k and mean reciprocal rank (MRR) metricsAssess cross-lingual retrieval performance (NL in one language, code in another)

Best for

Researchers building semantic code search systems

Teams developing code recommendation engines for IDEs

Organizations evaluating neural code retrieval models

Requires

Problem descriptions (natural language text)

Retrieval corpus with code implementations linked via src_uid

Retrieval model that accepts NL queries and returns ranked code results

Limitations

Retrieval corpus must be pre-indexed; no built-in indexing or embedding generation provided

Multiple correct implementations per problem not handled; evaluation assumes single ground-truth code per problem

Retrieval metrics (recall@k, MRR) do not measure code quality or efficiency; only presence in top-k results

What makes it unique

Provides NL-to-code retrieval evaluation with src_uid linking between problem descriptions and code corpus; supports multilingual retrieval (NL in any language, code in any of 17 languages) within unified benchmark framework

vs alternatives

Enables cross-lingual retrieval evaluation; execution-based validation not required (unlike code generation tasks), reducing computational overhead

code-to-code retrieval with semantic similarity matching

Medium confidence

Evaluates code-to-code retrieval models by measuring whether semantically equivalent code implementations are retrieved when querying with a given code snippet. The system stores multiple code implementations of the same problem in different languages, linked via src_uid, and measures retrieval accuracy by checking if semantically equivalent implementations appear in top-k results. Unlike NL-to-code retrieval, this task assesses whether models understand code semantics across language boundaries.

Solves for

Evaluate code clone detection and semantic code similarity modelsMeasure cross-language code equivalence detection (e.g., finding Python code equivalent to a Java snippet)Benchmark code-to-code retrieval on recall@k and MRR metricsAssess whether models understand functional equivalence despite syntactic differences

Best for

Researchers building code clone detection systems

Teams developing code deduplication tools

Organizations evaluating cross-language code similarity models

Requires

Code implementations in multiple languages linked via src_uid

Semantic equivalence labels (which implementations are equivalent)

Code-to-code retrieval model that accepts code snippets and returns ranked results

Limitations

Semantic equivalence must be pre-defined; no automatic detection of equivalent implementations

Multiple correct implementations per problem may have varying similarity; evaluation assumes binary equivalence

Language-specific idioms and library differences may cause false negatives (e.g., Python list comprehension vs. Java stream)

What makes it unique

Provides code-to-code retrieval evaluation across all 17 languages with src_uid linking between semantically equivalent implementations; enables cross-language code similarity assessment within unified benchmark

vs alternatives

Supports cross-language retrieval (not just same-language clone detection); unified infrastructure with other 6 tasks enables multi-task model evaluation

tag classification for code understanding and categorization

Medium confidence

Evaluates code understanding models by classifying code snippets into predefined categories (tags) based on problem descriptions and code implementations. The system provides code linked to problem descriptions via src_uid, along with ground-truth tags, allowing models to predict tags and evaluation to measure classification accuracy (precision, recall, F1). This task assesses whether models understand code semantics and can categorize problems by difficulty, algorithm type, or domain.

Solves for

Evaluate code understanding models on their ability to classify code by problem type or difficultyMeasure multi-label classification accuracy for code categorizationBenchmark models on understanding code semantics without executionAssess whether models can infer problem characteristics from code alone

Best for

Researchers building code understanding and classification models

Teams developing code recommendation systems that filter by problem type

Organizations evaluating code quality and complexity assessment tools

Requires

Code implementations linked to problem descriptions via src_uid

Ground-truth tags for each problem (predefined taxonomy)

Classification model that accepts code and outputs tag predictions

Limitations

Tag taxonomy must be pre-defined; no automatic tag generation or discovery

Multi-label classification with imbalanced tag distributions may skew metrics

Tag definitions may be ambiguous or subjective; inter-annotator agreement not measured

What makes it unique

Provides code classification evaluation as one of 7 integrated tasks; enables non-execution-based code understanding assessment alongside execution-based generation and translation tasks

vs alternatives

Integrated with other tasks in unified benchmark; enables multi-task model training and evaluation without requiring execution infrastructure

code compilation validation across 17 languages with compiler-specific error handling

Medium confidence

Evaluates whether code compiles successfully in its target language by running language-specific compilers (C/C++/C# via MSVC/GCC, Java via javac, Rust via rustc, etc.) within Docker containers. The system stores code linked to problem definitions via src_uid and measures compilation success rates, capturing compiler-specific error messages and exit codes. This task validates syntactic correctness and language-specific type checking without requiring execution against test cases.

Solves for

Measure code generation model accuracy on syntactic correctness and type safetyIdentify language-specific compilation issues (e.g., Rust borrow checker errors)Evaluate code translation models on compilation success before execution testingBenchmark models on producing compilable code across different language ecosystems

Best for

Researchers evaluating code generation models on syntactic correctness

Teams building code synthesis systems that need compilation validation

Organizations assessing code quality before execution testing

Requires

Docker with language-specific compilers/runtimes installed

ExecEval configuration mapping languages to compilation commands

Code samples linked to problem definitions via src_uid

Limitations

Compilation success does not guarantee functional correctness; requires separate execution testing

Compiler versions and flags vary across languages; configuration must be standardized per language

Compilation time varies significantly (Rust slower than Python); no caching of compilation artifacts

What makes it unique

Provides compilation validation as standalone task within unified benchmark; supports 17 languages with compiler-specific configuration in ExecEval, enabling standardized syntactic correctness measurement

vs alternatives

Integrated with other tasks; enables multi-stage evaluation (compilation → execution) within single framework

hugging face datasets api integration with automatic src_uid resolution

Medium confidence

Provides Python API for loading xCodeEval datasets from Hugging Face Hub with automatic resolution of src_uid links to problem descriptions and unit tests. The integration uses the datasets library to stream or download task-specific files, automatically joins them with centralized problem_descriptions.jsonl and unittest_db.json, and returns structured DatasetDict objects with all fields flattened. This approach eliminates manual data loading and linking logic, enabling researchers to load complete datasets in a few lines of code.

Solves for

Load xCodeEval task datasets with automatic src_uid resolution without manual joiningStream large datasets without downloading entire files to diskAccess datasets programmatically in Python for model training and evaluationVerify dataset integrity and completeness during loading

Best for

ML researchers using Python for model training and evaluation

Teams building training pipelines that need automatic data loading

Organizations using Hugging Face Hub for dataset distribution

Requires

Python 3.7+

Hugging Face datasets library (latest version)

Internet connectivity to access Hugging Face Hub

Limitations

Python-only; no native support for other languages (R, Julia, etc.)

Initial download may be slow for full dataset (25M examples); streaming mode has higher latency per record

Automatic linking adds overhead; manual Git LFS access may be faster for selective data access

What makes it unique

Integrates with Hugging Face datasets library to provide automatic src_uid resolution during loading; eliminates manual joining logic and enables streaming access to 25M examples without full download

vs alternatives

Simpler API than manual Git LFS access; automatic linking reduces boilerplate code; streaming support enables memory-efficient access to large datasets

execeval containerized execution engine with language-specific runtime configuration

Medium confidence

Provides Docker-based execution environment for running code in any of 17 supported languages with standardized compilation and execution pipelines. ExecEval accepts code, language identifier, and unit tests as input, compiles code using language-specific compilers (with configurable flags), executes compiled code against test cases, and returns structured execution outcomes (PASS, RUNTIME_ERROR, COMPILATION_ERROR, TIMEOUT) with error messages. Configuration maps each language to appropriate compiler, runtime, and execution parameters, enabling consistent evaluation across heterogeneous language ecosystems.

Solves for

Execute generated code in any of 17 languages with standardized compilation and test executionMeasure functional correctness by running code against unit testsCapture execution outcomes and error messages for debugging and analysisEvaluate code generation, translation, and repair models with execution-based metrics

Best for

Researchers evaluating code generation models with execution-based metrics

Teams building code synthesis systems that require functional validation

Organizations benchmarking multilingual code models

Requires

Docker (latest version) with sufficient disk space for language runtimes

ExecEval configuration files mapping languages to compilers and execution commands

Language-specific compilers/runtimes installed in Docker image

Limitations

Requires Docker runtime; cannot execute code without containerization

Compilation and execution time varies significantly across languages (Rust slower than Python)

No built-in caching of compilation artifacts; recompilation required for each execution

What makes it unique

Provides unified execution engine for 17 languages with standardized compilation and test execution pipelines; configuration-driven approach enables adding new languages without code changes

vs alternatives

Supports more languages than most code evaluation frameworks; containerization provides isolation and reproducibility; unified interface across heterogeneous language ecosystems

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 xCodeEval, ranked by overlap. Discovered automatically through the match graph.

Benchmark21

bigcode-models-leaderboard

bigcode-models-leaderboard — AI demo on HuggingFace

multi-language code generation task evaluationautomated code generation model benchmarking with standardized evaluation metrics

2 shared capabilities

Repository45

CodeGeeX

CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)

humaneval-x multilingual code generation benchmark with 820 problemsmultilingual code generation from natural language and partial code

2 shared capabilities

Model44

Codestral

Mistral's dedicated 22B code generation model.

multi-language humaneval evaluation across c++, bash, java, php, typescript, c#multi-language code generation from natural language instructions

2 shared capabilities

Repository44

CodeT5

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

humaneval benchmark evaluation with pass@k metricscodebleu metric computation for code generation quality

2 shared capabilities

Dataset48

CodeContests

13K competitive programming problems from AlphaCode research.

multi-language-solution-reference-corpustest-case-driven-code-evaluation-harness

2 shared capabilities

Benchmark42

Big Code Bench

Comprehensive code benchmark — 1,140 practical tasks with real library usage beyond HumanEval.

multi-split code generation task evaluation with pass@k metrics

1 shared capability

Best For

✓ML researchers evaluating multilingual code generation models
✓Teams building cross-language code synthesis systems
✓Organizations benchmarking LLM performance on functional correctness rather than syntactic similarity
✓Researchers analyzing how models perform across multiple tasks on the same problems
✓Teams building multi-task training pipelines that need consistent problem definitions
✓Data engineers optimizing storage and bandwidth for large-scale dataset distribution
✓Researchers evaluating code generation models with multiple samples
✓Teams comparing models on pass@k metrics (standard in code generation benchmarks)

Known Limitations

⚠ExecEval execution engine requires Docker containerization — cannot evaluate code without Docker runtime
⚠Evaluation latency scales with number of test cases and language compilation times; no built-in caching of compilation artifacts
⚠Limited to 17 languages; adding new languages requires compiler configuration and unittest_db extension
⚠Pass@k metrics require multiple generations per problem, increasing computational cost for large-scale evaluation
⚠Manual data access via Git LFS requires explicit src_uid linking logic; automatic linking only available through Hugging Face API
⚠Circular dependencies possible if problem_descriptions.jsonl or unittest_db.json are corrupted; no built-in validation of referential integrity

Requirements

Python 3.7+Docker (latest) for ExecEval execution engineHugging Face datasets library or Git LFS 2.0+ for data access16 GB+ RAM for processing large dataset splitsHugging Face datasets library (recommended) OR Git LFS 2.0+ for manual accessPython 3.7+ for programmatic linkingAccess to NTU-NLP-sg/xCodeEval Hugging Face repositoryCode generation model that can sample k independent generations per problem

Input / Output

Accepts: problem descriptions (natural language), generated code (text in any of 17 supported languages), unit test specifications (JSON format), task-specific dataset files (JSONL format with src_uid field), problem_descriptions.jsonl (centralized problem metadata), unittest_db.json (centralized unit test specifications), k (number of samples per problem, integer), generated code samples (k samples per problem), unit tests (JSON format), problem ID (src_uid), language identifier (for retrieving language-specific implementations), problem descriptions and unit tests, code generation model, generation parameters (k, beam width, temperature, etc.), source code (in language A), translated code (in language B), problem description (language-agnostic), unit tests (I/O-based format), buggy code (text in any of 17 supported languages), problem description, unit tests (must fail on buggy code), problem descriptions (natural language text), code implementations (text in any of 17 supported languages), retrieval model outputs (ranked list of code IDs), query code (text in any of 17 supported languages), code corpus (implementations in any of 17 supported languages), semantic equivalence labels (which codes are equivalent), code (text in any of 17 supported languages), ground-truth tags (categorical labels), language identifier (to select appropriate compiler), task name (string: 'program_synthesis', 'code_translation', etc.), split name (string: 'train', 'validation', 'test'), language identifier (string), unit tests (JSON format with input/output specifications), execution parameters (timeout, memory limit, etc.)

Produces: pass@k metrics (float 0-1), execution outcomes (PASS, RUNTIME_ERROR, COMPILATION_ERROR, TIMEOUT), detailed test case results with error messages, DatasetDict with resolved problem descriptions and unit tests, Structured records with flattened problem context and task-specific fields, pass@k metric (float 0-1, fraction of problems with at least one passing sample), per-problem results (which problems passed with which samples), execution outcomes for all samples, problem description (natural language text), input/output specifications (text), unit tests (JSON format with test cases and expected outputs), linked code implementations (in any of 17 languages), Phase 1: generated code samples (k per problem), Phase 2: execution outcomes (PASS, RUNTIME_ERROR, etc.), Phase 3: pass@k metrics and per-problem results, pass@k metrics for translation accuracy, execution outcomes per test case, error logs (compilation errors, runtime exceptions), repaired code (text), pass@k metrics (fraction of repairs passing all tests), recall@k metrics (fraction of queries with correct code in top-k), MRR (mean reciprocal rank of first correct result), retrieval rankings (ordered list of code IDs per query), recall@k metrics (fraction of queries with equivalent code in top-k), MRR (mean reciprocal rank of first equivalent result), tag predictions (categorical labels), classification metrics (precision, recall, F1 per tag), confusion matrices (tag prediction errors), compilation success/failure (boolean), compiler error messages (text), compilation time (milliseconds), DatasetDict with resolved fields, Structured records with problem descriptions, code, tests, and task-specific fields, execution outcome (PASS, RUNTIME_ERROR, COMPILATION_ERROR, TIMEOUT), error messages (compiler errors, runtime exceptions), execution time (milliseconds), test case results (per-test pass/fail status)

UnfragileRank

Adoption70%(35% weight)

Quality23%(25% weight)

Ecosystem40%(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

13 capabilities

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Multilingual code evaluation benchmark covering 17 programming languages with code generation, translation, retrieval, and understanding tasks, enabling cross-lingual assessment of code intelligence models.

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Capabilities13 decomposed

multilingual code generation benchmarking across 17 languages with execution-based validation

Medium confidence

Solves for

Best for

ML researchers evaluating multilingual code generation models

Teams building cross-language code synthesis systems

Organizations benchmarking LLM performance on functional correctness rather than syntactic similarity

Requires

Python 3.7+

Docker (latest) for ExecEval execution engine

Hugging Face datasets library or Git LFS 2.0+ for data access

Limitations

ExecEval execution engine requires Docker containerization — cannot evaluate code without Docker runtime

Evaluation latency scales with number of test cases and language compilation times; no built-in caching of compilation artifacts

Limited to 17 languages; adding new languages requires compiler configuration and unittest_db extension

What makes it unique

vs alternatives

Provides execution-based correctness measurement across more languages than HumanEval (Python-only) and with unified infrastructure for code translation and retrieval tasks, not just generation

src_uid-based cross-task data linking and problem deduplication

Medium confidence

Solves for

Best for

Researchers analyzing how models perform across multiple tasks on the same problems

Teams building multi-task training pipelines that need consistent problem definitions

Data engineers optimizing storage and bandwidth for large-scale dataset distribution

Requires

Hugging Face datasets library (recommended) OR Git LFS 2.0+ for manual access

Python 3.7+ for programmatic linking

Access to NTU-NLP-sg/xCodeEval Hugging Face repository

Limitations

Manual data access via Git LFS requires explicit src_uid linking logic; automatic linking only available through Hugging Face API

Circular dependencies possible if problem_descriptions.jsonl or unittest_db.json are corrupted; no built-in validation of referential integrity

src_uid format and collision handling not documented; potential issues if src_uid generation changes between dataset versions

What makes it unique

vs alternatives

pass@k metric computation for multi-sample code generation evaluation

Medium confidence

Solves for

Best for

Researchers evaluating code generation models with multiple samples

Teams comparing models on pass@k metrics (standard in code generation benchmarks)

Organizations assessing model reliability and robustness

Requires

Code generation model that can sample k independent generations per problem

ExecEval execution engine for running generated code

Unit tests for each problem

Limitations

Computational cost scales linearly with k; large k values require significant execution time

Pass@k assumes independence between samples; correlated errors may inflate pass@k estimates

Metric does not measure code quality or efficiency; only binary pass/fail per problem

What makes it unique

Integrates pass@k computation into unified evaluation pipeline alongside execution outcomes; supports pass@k for all 7 tasks (synthesis, translation, APR, etc.), not just code generation

vs alternatives

Standard metric in code generation benchmarks; accounts for sampling variance; enables fair comparison across models with different sampling strategies

multilingual problem description and unit test corpus with 7,500 unique problems

Medium confidence

Solves for

Best for

Researchers building code generation and understanding models

Teams creating multilingual training datasets

Organizations benchmarking code models across diverse problem types

Requires

Access to problem_descriptions.jsonl and unittest_db.json files

Hugging Face datasets library or Git LFS for data access

Limitations

Problem descriptions are in English only; no multilingual problem statements

Unit tests must be language-agnostic (I/O-based); domain-specific tests not supported

Problem difficulty and algorithm type not explicitly labeled; must be inferred from description

What makes it unique

Provides 7,500 problems with consistent unit tests across 17 languages; centralized storage via src_uid linking eliminates duplication and ensures consistency across 7 tasks and 25M training examples

vs alternatives

Larger and more diverse than HumanEval (164 problems); supports more languages and tasks; consistent test suites across languages enable fair cross-language evaluation

three-phase evaluation pipeline with generation, execution, and metrics computation

Medium confidence

Solves for

Best for

Researchers evaluating code generation models with standardized pipeline

Teams building evaluation infrastructure for code models

Organizations benchmarking multiple models with consistent methodology

Requires

Code generation model for Phase 1

ExecEval execution engine for Phase 2

Evaluation script to compute metrics in Phase 3

Limitations

Phase separation adds complexity; requires careful coordination between phases

Intermediate results must be stored; large-scale evaluation requires significant disk space

No built-in support for streaming evaluation; all results must be materialized

What makes it unique

Separates generation, execution, and metrics computation into distinct phases; enables modular evaluation and supports different generation strategies without pipeline modification

vs alternatives

Modular design enables reuse of phases for different tasks; intermediate results support debugging and analysis; standardized pipeline ensures consistent evaluation across models

code translation evaluation with language-pair specific test execution

Medium confidence

Solves for

Best for

Researchers building code translation models across multiple language pairs

Teams evaluating automated code migration tools (e.g., Python to Go)

Organizations assessing code modernization pipelines with execution-based validation

Requires

Docker with compilers/runtimes for both source and target languages

ExecEval configuration mapping language pairs to appropriate compilation and execution commands

Unit tests compatible with both source and target language I/O conventions

Limitations

Test suite must be language-agnostic (e.g., I/O-based tests) to apply to both source and target languages; domain-specific tests may not translate

Compilation time varies significantly across languages (Rust slower than Python); evaluation latency not uniform across language pairs

Runtime behavior differences (e.g., integer overflow handling) may cause false negatives if tests assume source language semantics

What makes it unique

vs alternatives

automatic program repair (apr) evaluation with test-driven validation

Medium confidence

Solves for

Best for

Researchers developing automated program repair techniques

Teams building code quality tools that suggest fixes for failing tests

Organizations evaluating AI-assisted debugging and code fixing systems

Requires

Buggy code samples linked to problem definitions via src_uid

Comprehensive unit test suites that fail on buggy code

Docker with ExecEval for execution-based validation

Limitations

Buggy code variants must be pre-generated or manually curated; no automatic bug injection mechanism provided

Test suite must be comprehensive enough to catch the injected bugs; weak tests may accept incorrect repairs

Repair evaluation cannot distinguish between minimal fixes and complete rewrites; no code similarity metrics included

What makes it unique

vs alternatives

Enables cross-language APR evaluation with consistent test suites; execution-based approach ensures repairs are functionally correct, not just syntactically plausible

natural language to code retrieval with semantic matching

Medium confidence

Solves for

Best for

Researchers building semantic code search systems

Teams developing code recommendation engines for IDEs

Organizations evaluating neural code retrieval models

Requires

Problem descriptions (natural language text)

Retrieval corpus with code implementations linked via src_uid

Retrieval model that accepts NL queries and returns ranked code results

Limitations

Retrieval corpus must be pre-indexed; no built-in indexing or embedding generation provided

Multiple correct implementations per problem not handled; evaluation assumes single ground-truth code per problem

Retrieval metrics (recall@k, MRR) do not measure code quality or efficiency; only presence in top-k results

What makes it unique

vs alternatives

Enables cross-lingual retrieval evaluation; execution-based validation not required (unlike code generation tasks), reducing computational overhead

code-to-code retrieval with semantic similarity matching

Medium confidence

Solves for

Best for

Researchers building code clone detection systems

Teams developing code deduplication tools

Organizations evaluating cross-language code similarity models

Requires

Code implementations in multiple languages linked via src_uid

Semantic equivalence labels (which implementations are equivalent)

Code-to-code retrieval model that accepts code snippets and returns ranked results

Limitations

Semantic equivalence must be pre-defined; no automatic detection of equivalent implementations

Multiple correct implementations per problem may have varying similarity; evaluation assumes binary equivalence

Language-specific idioms and library differences may cause false negatives (e.g., Python list comprehension vs. Java stream)

What makes it unique

vs alternatives

Supports cross-language retrieval (not just same-language clone detection); unified infrastructure with other 6 tasks enables multi-task model evaluation

tag classification for code understanding and categorization

Medium confidence

Solves for

Best for

Researchers building code understanding and classification models

Teams developing code recommendation systems that filter by problem type

Organizations evaluating code quality and complexity assessment tools

Requires

Code implementations linked to problem descriptions via src_uid

Ground-truth tags for each problem (predefined taxonomy)

Classification model that accepts code and outputs tag predictions

Limitations

Tag taxonomy must be pre-defined; no automatic tag generation or discovery

Multi-label classification with imbalanced tag distributions may skew metrics

Tag definitions may be ambiguous or subjective; inter-annotator agreement not measured

What makes it unique

Provides code classification evaluation as one of 7 integrated tasks; enables non-execution-based code understanding assessment alongside execution-based generation and translation tasks

vs alternatives

Integrated with other tasks in unified benchmark; enables multi-task model training and evaluation without requiring execution infrastructure

code compilation validation across 17 languages with compiler-specific error handling

Medium confidence

Solves for

Best for

Researchers evaluating code generation models on syntactic correctness

Teams building code synthesis systems that need compilation validation

Organizations assessing code quality before execution testing

Requires

Docker with language-specific compilers/runtimes installed

ExecEval configuration mapping languages to compilation commands

Code samples linked to problem definitions via src_uid

Limitations

Compilation success does not guarantee functional correctness; requires separate execution testing

Compiler versions and flags vary across languages; configuration must be standardized per language

Compilation time varies significantly (Rust slower than Python); no caching of compilation artifacts

What makes it unique

vs alternatives

Integrated with other tasks; enables multi-stage evaluation (compilation → execution) within single framework

hugging face datasets api integration with automatic src_uid resolution

Medium confidence

Solves for

Best for

ML researchers using Python for model training and evaluation

Teams building training pipelines that need automatic data loading

Organizations using Hugging Face Hub for dataset distribution

Requires

Python 3.7+

Hugging Face datasets library (latest version)

Internet connectivity to access Hugging Face Hub

Limitations

Python-only; no native support for other languages (R, Julia, etc.)

Initial download may be slow for full dataset (25M examples); streaming mode has higher latency per record

Automatic linking adds overhead; manual Git LFS access may be faster for selective data access

What makes it unique

vs alternatives

Simpler API than manual Git LFS access; automatic linking reduces boilerplate code; streaming support enables memory-efficient access to large datasets

execeval containerized execution engine with language-specific runtime configuration

Medium confidence

Solves for

Best for

Researchers evaluating code generation models with execution-based metrics

Teams building code synthesis systems that require functional validation

Organizations benchmarking multilingual code models

Requires

Docker (latest version) with sufficient disk space for language runtimes

ExecEval configuration files mapping languages to compilers and execution commands

Language-specific compilers/runtimes installed in Docker image

Limitations

Requires Docker runtime; cannot execute code without containerization

Compilation and execution time varies significantly across languages (Rust slower than Python)

No built-in caching of compilation artifacts; recompilation required for each execution

What makes it unique

Provides unified execution engine for 17 languages with standardized compilation and test execution pipelines; configuration-driven approach enables adding new languages without code changes

vs alternatives

Supports more languages than most code evaluation frameworks; containerization provides isolation and reproducibility; unified interface across heterogeneous language ecosystems

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

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xCodeEval

Capabilities13 decomposed

multilingual code generation benchmarking across 17 languages with execution-based validation

src_uid-based cross-task data linking and problem deduplication

pass@k metric computation for multi-sample code generation evaluation

multilingual problem description and unit test corpus with 7,500 unique problems

three-phase evaluation pipeline with generation, execution, and metrics computation

code translation evaluation with language-pair specific test execution

automatic program repair (apr) evaluation with test-driven validation

natural language to code retrieval with semantic matching

code-to-code retrieval with semantic similarity matching

tag classification for code understanding and categorization

code compilation validation across 17 languages with compiler-specific error handling

hugging face datasets api integration with automatic src_uid resolution

execeval containerized execution engine with language-specific runtime configuration

Related Artifactssharing capabilities

bigcode-models-leaderboard

CodeGeeX

Codestral

CodeT5

CodeContests

Big Code Bench

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to xCodeEval

Are you the builder of xCodeEval?

Get the weekly brief

Data Sources

xCodeEval

Capabilities13 decomposed

multilingual code generation benchmarking across 17 languages with execution-based validation

src_uid-based cross-task data linking and problem deduplication

pass@k metric computation for multi-sample code generation evaluation

multilingual problem description and unit test corpus with 7,500 unique problems

three-phase evaluation pipeline with generation, execution, and metrics computation

code translation evaluation with language-pair specific test execution

automatic program repair (apr) evaluation with test-driven validation

natural language to code retrieval with semantic matching

code-to-code retrieval with semantic similarity matching

tag classification for code understanding and categorization

code compilation validation across 17 languages with compiler-specific error handling

hugging face datasets api integration with automatic src_uid resolution

execeval containerized execution engine with language-specific runtime configuration

Related Artifactssharing capabilities

bigcode-models-leaderboard

CodeGeeX

Codestral

CodeT5

CodeContests

Big Code Bench

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to xCodeEval

Are you the builder of xCodeEval?

Get the weekly brief

Data Sources