Workflow DAG Scheduling Code Understanding Dataset

#code comprehension #dependency graph analysis #task execution reasoning #workflow orchestration #DAG scheduling systems #task dependency management

5200
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-10-25

AI Analysis & Value Prop

This dataset covers source code for workflow task dependency graph construction, dependency readiness checks, topological processing, and downstream task triggering. It pairs relevant scheduling code excerpts with supervised questions and answers about dependencies and execution behavior. The content supports code fine-tuning, source code question answering, and evaluation of scheduling logic understanding in distributed task and job scheduling systems.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
answer	string	A direct answer grounded in the provided source code.
question	string	A question about the DAG logic or execution behavior shown in the source code.
source_code	string	A code excerpt implementing workflow DAG construction, dependency checks, topological processing, or downstream task scheduling.
execution_behavior	string	An explanation of how the code schedules or triggers successor tasks, including relevant conditions and state changes.
dependency_analysis	string	An explanation of how the code represents task dependencies, checks readiness, or processes tasks in topological order.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: It contains source code excerpts related to workflow DAG scheduling, along with questions, answers, and analyses about task dependencies, readiness conditions, topological processing, and downstream task triggering.

Which capabilities can it support?: The dataset is suitable for supervised code fine-tuning and code understanding evaluation. It can help models explain dependency handling, scheduling conditions, and task execution behavior based on source code.

Does the dataset specify a fixed size or programming language?: The available information does not specify the sample count, programming languages, or source coverage. Check the product details or sample records to confirm the exact specifications and scope.

How can I obtain the dataset, and what are its licensing terms?: Refer to the dataset product page for access methods, available formats, and licensing terms. Review the applicable license and restrictions before using it for commercial training, redistribution, or public release.

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Cite this Work

@dataset{Mobiusi2026,
  title={Workflow DAG Scheduling Code Understanding Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/45651624fd5f28c17d2cbe65c39b4159},
  urldate={2026-10-21},
  keywords={workflow DAG dataset,task scheduling code,code understanding training data,dependency analysis,topological scheduling source code,job scheduling Q&amp;A,code SFT dataset,distributed scheduling systems},
  version={1.0}
}

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