README.md

metadata

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.