| --- |
| license: mit |
| task_categories: |
| - text-generation |
| - other |
| language: |
| - en |
| pretty_name: IndustryCode |
| size_categories: |
| - n<1K |
| --- |
| |
| # IndustryCode |
|
|
| IndustryCode is an anonymized benchmark dataset for evaluating large language models on industry-oriented code generation tasks. |
|
|
| ## Overview |
|
|
| IndustryCode is designed to evaluate code generation for realistic industrial problems rather than general-purpose software exercises. It spans 20 subdomains across four major sectors: Finance, Automation, Aerospace, and Remote Sensing, and supports four programming languages: Python, C++, MATLAB, and Stata. |
|
|
| The dataset contains: |
|
|
| - 125 main problems |
| - 579 sub-problems |
| - 4 programming languages |
|
|
| Each record represents one main industrial coding task together with decomposed sub-steps and evaluation artifacts. |
|
|
| ## Dataset Structure |
|
|
| The dataset is distributed as JSONL files grouped by programming language and split: |
|
|
| - `c++_jsonl/test.jsonl` |
| - `c++_jsonl/validation.jsonl` |
| - `python_jsonl/test.jsonl` |
| - `python_jsonl/validation.jsonl` |
| - `matlab_jsonl/test.jsonl` |
| - `matlab_jsonl/validation.jsonl` |
| - `stata_jsonl/test.jsonl` |
| - `stata_jsonl/validation.jsonl` |
|
|
| Split counts: |
|
|
| - C++: 25 test, 9 validation |
| - Python: 63 test, 8 validation |
| - MATLAB: 16 test, 1 validation |
| - Stata: 2 test, 1 validation |
|
|
| ## Domain Coverage |
|
|
| Programming-language coverage in the released benchmark includes: |
|
|
| | Programming Language | Covered subfields | |
| |---|---| |
| | Python | Chemical Manufacturing, General Manufacturing, Finance and Insurance, Machinery Manufacturing, Software Publishers, Electronic Product Manufacturing, Medical Equipment Manufacturing, Information, Transportation and Warehousing | |
| | C++ | Construction, Information, Mining, Machinery Manufacturing, Electronic Product Manufacturing, Transportation and Warehousing | |
| | MATLAB | General Manufacturing, Transportation and Warehousing, Utilities, Finance and Insurance, Aerospace Product Manufacturing, Ship and Boat Building | |
| | Stata | Finance and Insurance | |
|
|
| ## Dataset Creation |
|
|
| IndustryCode sources challenging coding problems from authentic industrial production environments and transforms them into anonymized benchmark instances. Dataset construction follows a four-phase pipeline: |
|
|
| 1. **Problem selection**: representative industrial coding tasks are selected across diverse domains, including system simulation, data processing, and computational methods. |
| 2. **Manual revision and difficulty enhancement**: problems are reformulated and hardened with mathematical constraints, algorithmic complexity, engineering coupling, and domain-specific design patterns. |
| 3. **Evaluation system design**: each problem is paired with execution-oriented I/O test cases and, where needed, semantic evaluation criteria. |
| 4. **Iterative verification**: automated testing, domain review, and repeated refinement are used to improve consistency and evaluation quality. |
|
|
| ## Fields |
|
|
| Each JSONL row describes one main industrial coding problem and its decomposed sub-problems. |
|
|
| - `problem_name`: problem title |
| - `problem_id`: language-specific problem identifier |
| - `problem_description_main`: natural-language description of the main problem |
| - `problem_background_main`: optional domain background for the main problem |
| - `problem_io`: input/output specification |
| - `required_dependencies`: required libraries, packages, or imports |
| - `sub_steps`: list of sub-problems derived from the main problem |
| - `general_solution`: optional solution field |
| - `general_tests`: optional general test field |
|
|
| Each item in `sub_steps` may contain: |
|
|
| - `step_number` |
| - `step_description_prompt` |
| - `step_background` |
| - `ground_truth_code` |
| - `function_header` |
| - `test_cases` |
| - `return_line` |
|
|
| ## Intended Use |
|
|
| The dataset is intended for: |
|
|
| - benchmarking large language models on industry-oriented code generation tasks |
| - comparing task-level code generation performance across languages and domains |
| - studying problem decomposition and step-wise code synthesis |
| - supporting evaluations reported in anonymous peer review submissions |
|
|
| The dataset is not intended to serve as a certification benchmark for production readiness, safety-critical deployment, or repository-scale software maintenance. |
|
|
| ## Responsible AI Notes |
|
|
| The dataset is designed for evaluating code generation systems. It does not intentionally include personally identifiable information or human-subject data. The released benchmark instances are anonymized task descriptions and evaluation artifacts rather than raw proprietary repositories. |
|
|
| Known limitations include: |
|
|
| - imbalanced language coverage |
| - underrepresentation of collaborative or infrastructure-dependent engineering tasks |
| - partial dependence on benchmarkable task formulations rather than full production workflows |
|
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| Users should inspect generated code before execution, run generated code in a sandboxed environment, and consider domain-specific safety, compliance, and correctness requirements before practical use. |
|
|
| ## Splits |
|
|
| Validation files are intended for development and prompt calibration. Test files are intended for final benchmark evaluation. |
|
|
| ## Citation |
|
|
| Citation information will be provided after the anonymous review period. |
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|
