JS/TS-Smell - A Curated, Metric-Enriched Dataset for Code Smells and Anti-Patterns in JavaScript and TypeScript

JS/TS-Smell: A Curated, Metric-Enriched Dataset for Code Smells and Anti-Patterns in JavaScript and TypeScript This dataset provides a rigorously curated collection of annotated JavaScript and TypeScript code snippets, focusing on widely recognized code smells and anti-patterns. It was constructed from actively maintained, high-quality open-source projects retrieved from GitHub, selected using strict inclusion criteria to ensure representativeness, diversity, and sustainability. Each snippet is annotated for selected smells and anti-patterns, guided by established taxonomies (Fowler, 2019; Brown et al., 1998), and validated through expert-based consensus with substantial inter-annotator agreement. In addition, the dataset is enriched with a comprehensive set of software metrics at both class and method/function levels, including measures of size, complexity, cohesion, and coupling. These metrics provide objective, reproducible features that support empirical software engineering research and machine learning applications. The dataset is released in two structured CSV files: Class-level metrics and annotations Method/function-level metrics and annotations Potential applications include: Benchmarking and training machine learning and large language model–based smell detection tools. Empirical studies on software maintainability and quality assessment. Replication and extension of research on code smells and anti-patterns in modern JavaScript/TypeScript systems. This resource addresses a critical gap in the availability of high-quality, language-specific datasets beyond Java, and aims to support both academic research and industrial applications in software quality analysis.