Scheduler Dependency Logic Bug Repair Dataset

#code reasoning #bug localization #root cause analysis #code patch generation #post-fix behavior reasoning #regression test design #distributed task scheduling #workflow engine maintenance #scheduler bug repair #code review #software regression testing

Records: 7690
Size: 0.5GB
Format: JSON
Provider: MOBIUSI INC
Updated: v1.02026-11-04

AI Analysis & Value Prop

This dataset focuses on dependency logic defects in distributed task and job schedulers, providing relevant source code, issue descriptions, reproduction tests, issue localization, root cause analysis, repair patches, and post-fix behavior reasoning. Examples cover incorrect dependency checks, downstream tasks running too early, tasks being skipped incorrectly, and dependency state not being cleared properly after retries. The collection supports training models to reason about scheduler states and dependencies, diagnose code defects, and generate targeted fixes, as well as code supervised fine-tuning, patch generation, and regression evaluation.

Overview

Dataset Insights

Preview

Sample Examples

sample.json

{}

Schema

Technical Specifications

Field	Type	Description
file_name	string	File name
test_cases	array	Inputs, states, and expected behavior used to reproduce scheduler dependency defects and verify repairs.
source_code	string	Source code containing dependency checks, task scheduling, retry handling, or downstream execution logic.
repair_patch	string	Fixed code or a code patch addressing the localized dependency logic defect.
localized_issue	object	Identifies the code location and suspected condition responsible for the dependency logic error.
issue_description	string	A natural language description of defects such as incorrect dependency checks, premature downstream execution, incorrect task skipping, or dependencies not being cleared after retries.
validation_result	object	Records behavioral checks passed by the repair patch and any potential regression risks.
behavior_reasoning	object	Reasons about scheduler behavior across task states and retry scenarios, including the dependency invariants that should hold after the fix.
repair_explanation	string	Explains how the patch corrects dependency checks, prevents premature downstream execution, avoids incorrect skipping, and handles dependency clearing after retries.
root_cause_analysis	string	Explains the logic error and trigger mechanism involving dependency state, task state, retry state, or evaluation order.

Trust

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

FAQ

Frequently Asked Questions

Which scheduler defects does the dataset cover?: Examples focus on task dependency logic, including incorrect dependency checks, downstream tasks running prematurely, tasks being skipped incorrectly, and dependency state not being cleared properly after retries. Samples can combine code, observed symptoms, and test cases to help analyze trigger conditions and impact.

What repair and validation information is provided?: Fields may include issue localization, root cause analysis, repair patches, repair explanations, post-fix behavior reasoning, and validation results. Test cases are intended to reproduce defects and check repaired behavior; the exact validation coverage depends on the content of each sample.

Which training and evaluation tasks are supported?: The dataset supports code supervised fine-tuning and tasks such as scheduler dependency reasoning, bug localization, root cause analysis, code patch generation, and repair explanation. It can also support regression evaluations for downstream execution order, retry handling, and dependency invariants.

How can I obtain the data and confirm its license?: Check the dataset product page or the designated delivery channel for available formats, access options, and license terms. Before use, confirm whether the license covers model training, evaluation, commercial use, and code redistribution; the applicable rights are defined by the product listing or formal license agreement.

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Cite this Work

@dataset{Mobiusi2026,
  title={Scheduler Dependency Logic Bug Repair Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/6707bd68ecb3cf93fda7725d4bc64bf1?dataset_task_cate_id=6},
  urldate={2026-10-28},
  keywords={scheduler dependency logic,job scheduling code,dependency bug repair,code reasoning dataset,bug localization training,patch generation,distributed task scheduling,code SFT,scheduler regression testing},
  version={1.0}
}

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