"GoBug Defect Dataset"

"To address the growing need for empirical resources in modern software ecosystems, we present GoBug, the first large-scale, multi-level dataset for software defect prediction (SDP) in the Go programming language (Go). The dataset was constructed from 16 prominent, high-impact open-source Go projects from GitHub, including Kubernetes, Terraform, and InfluxDB.GoBug provides labeled data at three distinct levels of granularity: commit, file, and method. A key feature of this dataset is its rich and comprehensive set of metrics, which combines traditional process metrics (e.g., code churn, author count) and static code metrics (e.g., cyclomatic complexity, LOC) with a novel suite of Go-specific metrics. These language-aware features, such as goroutine_count, channel_count, and error_handling_count, were accurately extracted by performing static analysis on the Abstract Syntax Tree (AST) of each source file.The ground truth for labeling was established using a high-confidence, reproducible methodology. We first identified bug-fixing commits by leveraging human-curated \"bug\" labels from GitHub Pull Requests. Subsequently, we applied a refactoring-aware version of the Sliwerski-Zimmermann-Zeller (SZZ) algorithm to pinpoint the corresponding bug-introducing commits.This dataset is intended to serve as a foundational, standardized benchmark to enable and accelerate reproducible research into software quality, testing, and defect prediction within the Go ecosystem."