Client Cache Consistency Code Verification Dataset

#Code verification #Patch correctness evaluation #Cache risk detection #Root-cause analysis #Distributed file systems #Client cache review #Patch evaluation #Consistency defect analysis

8050
0.5GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

This dataset focuses on client-side cache consistency in distributed file systems, providing related source code and candidate patches across cache reads and writes, refreshes, writebacks, lease invalidation, and read-write path coordination. Structured fields capture cache operations, risks such as stale reads and lost writes, root-cause analyses, and patch verification results, supporting the evaluation of code analysis and repair capabilities. It is suited to code verification, defect localization, and reviews of client cache implementations.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
source_code	string	Source code under analysis related to client caching, lease invalidation, cache refresh, writeback, or read-path coordination.
candidate_patch	string	A proposed patch or code change intended to fix a cache consistency issue.
stale_read_risk	boolean	Indicates whether a code path could return stale data before the cache is invalidated or refreshed.
write_loss_risk	boolean	Indicates whether concurrent writes, writeback, or cache overwrites could cause client write data to be lost.
patch_effectiveness	boolean	Indicates whether the candidate patch effectively eliminates or reduces the identified client cache consistency risks.
root_cause_analysis	string	Explains the causes of identified consistency errors, such as missed invalidation, ordering errors, race conditions, or insufficient locking or version control.
consistency_risk_score	number	A consistency risk score based on stale reads, lost writes, missed cache invalidations, and concurrency timing issues.
detected_cache_operations	string	Cache read, write, refresh, invalidation, lease renewal, or writeback operations identified in the source code and patch.
patch_verification_result	string	Describes whether the candidate patch covers the relevant failure paths and summarizes verification of fixes for stale reads, lost writes, and missed cache invalidations.
invalidation_omission_risk	boolean	Indicates whether a data update could fail to trigger invalidation of the relevant cache, lease, or read path.
lease_invalidation_behavior	string	Describes the code's behavior for lease expiration, active invalidation, failed renewal, and invalidation propagation.
read_write_path_coordination	string	Analyzes ordering, synchronization, and concurrency coordination among read paths, write paths, cache refresh, and writeback.

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 includes source code and candidate patches related to client caching, lease invalidation, cache refresh, writeback, and read-write path coordination. Structured fields capture risk assessments, root-cause analyses, and patch verification results.

What issues can the dataset help verify?: It can help assess whether code may cause stale reads, lost concurrent writes, or missed cache invalidations. It also supports analysis of lease behavior, read-write ordering, and whether candidate patches cover the relevant failure paths.

Does the dataset specify a fixed size or programming language?: The available information does not specify the sample count or programming language coverage. Review the product page for dataset specifications and examples to confirm compatibility with your codebase and evaluation workflow.

How can I access the dataset, and what are the licensing terms?: Access methods, available formats, and licensing scope are determined by the product page and accompanying license terms. Check the applicable terms before using the dataset commercially or redistributing it.

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Cite this Work

@dataset{Mobiusi2026,
  title={Client Cache Consistency Code Verification Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/0420a465c93f3121a6b762c7ae86f7d4},
  urldate={2026-10-27},
  keywords={client cache consistency,distributed file systems,code verification dataset,cache invalidation analysis,stale read detection,lost write detection,patch verification,root-cause analysis},
  version={1.0}
}

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