Artifact- and Data-Driven Dependency Reasoning Dataset

#code reasoning #dependency extraction #execution order prediction #code supervised fine-tuning #distributed task scheduling #service orchestration #workflow analysis #code understanding

8055
0.5GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

This dataset contains source code from distributed task scheduling and service orchestration scenarios, together with identified task nodes, artifact nodes, and dependency edges. Samples focus on task inputs and outputs, shared artifacts, data keys, file markers, and intermediate-result checks to help models infer implicit task dependencies, execution order, and code-based explanations. It supports code supervised fine-tuning, dependency extraction, and scheduling workflow analysis.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
task_nodes	array	Independently executable task units identified in the source code, including units that produce or consume data or perform checks.
source_code	string	Source code from distributed scheduling or service orchestration systems, provided as the input for dependency reasoning.
artifact_nodes	array	Data artifacts passed between tasks, such as files, shared objects, data records, data keys, or intermediate results.
dependency_edges	array	Directed task dependencies inferred from artifact production and consumption relationships.
validation_checks	array	Validation results used to check whether task relationships, artifact relationships, and execution sequences are consistent.
execution_sequence	array	Task execution order obtained by topologically sorting the dependency edges.
overall_confidence	number	A confidence score for the overall task identification, artifact matching, dependency inference, and execution order, ranging from 0 to 1.
reasoning_explanation	string	An overall explanation of data production and consumption, shared artifacts, and implicit execution order in the source code.

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?: The data is built around source code for distributed scheduling and service orchestration. It includes task nodes, artifact nodes, dependency edges, execution order, reasoning explanations, consistency checks, and overall confidence.

How are dependencies between tasks represented?: Task relationships are identified from code that produces or consumes data, shares artifacts, or performs checks, then represented as directed edges. Execution order is derived by topologically sorting those dependencies.

What information is available about data quality and scale?: The fields include dependency consistency checks and an overall confidence score ranging from 0 to 1, which can help assess reasoning outputs. The current product information does not specify sample count, code coverage, or detailed format, so consult the dataset documentation before use.

How can I obtain the data, and what are the license terms?: Use the acquisition channel listed on the product page and verify the delivery format, permitted uses, and commercial terms for the relevant version before use. No specific license terms are provided in the current information.

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Cite this Work

@dataset{Mobiusi2026,
  title={Artifact- and Data-Driven Dependency Reasoning Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/6531ef6c0cf1ef515905aeed63560562},
  urldate={2026-10-28},
  keywords={code dependency reasoning,distributed task scheduling,data-driven orchestration,source code analysis,task execution order,artifact relation extraction,workflow dependencies,code supervised fine-tuning},
  version={1.0}
}

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