Docker File-level Defect Prediction Dataset for Software Engineering

A dataset for predicting defects at the file level within Docker projects. It is hosted on Kaggle, but the specific number of rows, columns, and data collection details are not provided in the available metadata. The dataset's content and structure require verification after download.

Use Cases

• Train a classifier to predict defective files in Docker projects (inferred from domain, verify after download)
• Analyze code metrics for correlation with defect introduction in containerized environments (inferred from domain, verify after download)
• Benchmark machine learning models for software reliability prediction (inferred from domain, verify after download)

Strengths

• Published on Kaggle, a major platform for data science resources.

Limitations

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

Provenance

Source

Kaggle

Collection Method

How gathered, or null.

Time Range

Temporal coverage, or null.

Freshness

Last update date is unknown; freshness unverified.

Geography

Spatial coverage, or null.