---
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

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.