Kubernetes File-level Defect Prediction Dataset

A dataset for predicting defects at the file level within Kubernetes projects. It was published on Kaggle, but the author, collection method, and specific size are not detailed in the provided metadata. The dataset's content and structure require verification after download.

Use Cases

• Train a classifier to predict bug-prone files in Kubernetes repositories (inferred from domain, verify after download)
• Analyze correlations between code metrics and defect likelihood in container orchestration projects (inferred from domain, verify after download)
• Benchmark machine learning models for software defect prediction tasks (inferred from domain, verify after download)

Strengths

• Published on Kaggle, a platform with established data sharing infrastructure.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file format, and license are unknown, which may limit suitability assessment.

Provenance

Source

Kaggle

Collection Method

Unknown

Time Range

Unknown

Freshness

Last update date is unknown; freshness unverified.

Geography

Unknown