xCodeEval vs cua

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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)

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.

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

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.

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

Captures desktop screenshots and feeds them to 100+ integrated vision-language models (Claude, GPT-4V, Gemini, local models via adapters) to reason about UI state and determine appropriate next actions. Uses a unified message format (Responses API) across heterogeneous model providers, enabling the agent to understand visual context and generate structured action commands without brittle selector-based logic.

Unique: Implements a unified Responses API message format abstraction layer that normalizes outputs from 100+ heterogeneous VLM providers (native computer-use models like Claude, composed models via grounding adapters, and local model adapters), eliminating provider-specific parsing logic and enabling seamless model swapping without agent code changes.

vs alternatives: Broader model coverage and provider flexibility than Anthropic's native computer-use API alone, with explicit support for local/open-source models and a standardized message format that decouples agent logic from model implementation details.

Provisions isolated execution environments across macOS (via Lume VMs), Linux (Docker), Windows (Windows Sandbox), and host OS, with unified provider abstraction. Handles VM/container lifecycle (creation, snapshot management, cleanup), resource allocation, and OS-specific action handlers (keyboard/mouse events, clipboard, file system access) through a pluggable provider architecture that abstracts platform differences.

Unique: Implements a pluggable provider architecture with unified Computer interface that abstracts OS-specific action handlers (macOS native events via Lume, Linux X11/Wayland via Docker, Windows input simulation via Windows Sandbox API), enabling single agent code to target multiple platforms. Includes Lume VM management with snapshot/restore capabilities for deterministic testing.

vs alternatives: More comprehensive OS coverage than single-platform solutions; Lume provider offers native macOS VM support with snapshot capabilities unavailable in Docker-only alternatives, while unified provider abstraction reduces code duplication vs. platform-specific agent implementations.