Code Smell Discussions from OpenStack and Qt Code Reviews

1,539 code reviews discussing code smells were retrieved from the OpenStack and Qt communities. This replication package contains the data and results from a 2021 study published in the Empirical Software Engineering Journal. It includes coded data, extracted refactoring actions, and Python scripts used for keyword-based mining.

Use Cases

• Train classifiers to detect code smell discussions based on review comment keywords.
• Analyze the relationship between specific refactoring actions and code smell types.
• Study developer-reviewer disagreements when code smells are identified but ignored.
• Replicate keyword-based mining approaches for code smell detection in review comments.

Strengths

• Includes 1,539 code reviews specifically discussing code smells.
• Provides coded qualitative data analyzed with the MAXQDA tool.
• Contains Python scripts for reproducing the keyword search and data extraction methodology.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count beyond the 1,539 reviews is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

OpenStack and Qt code review systems.

Collection Method

Retrieved via Python scripts searching for code smell keywords in review comments.