Quality Assurance Awareness in Open Source GitHub Projects

Ali Khatami from Delft University of Technology created a dataset for the paper "Quality Assurance Awareness in Open Source Software Projects on GitHub", submitted to the 23rd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM) in 2023. The dataset likely contains metrics and analyses related to quality assurance practices. Its specific size, row count, and column details are not provided in the available metadata.

Use Cases

• Analyzing the adoption of quality assurance practices based on project metadata likely present in the data.
• Studying correlations between project characteristics and quality assurance awareness based on the described analysis.
• Benchmarking open-source projects on GitHub for software quality metrics suggested by the dataset's purpose.

Strengths

• Dataset is associated with a peer-reviewed academic paper submitted to the IEEE SCAM 2023 conference.
• The data originates from a specific, relevant platform (GitHub) for studying open-source software.

Limitations

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

Provenance

Source

Delft University of Technology

Collection Method

Analysis of open-source software projects on GitHub, as described in the associated paper.