ArticleScientific data2025
SmellyCode++: Multi-Label Dataset for Code Smell Detection.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
contextCode smells indicate poor software design, affecting maintainability. Accurate detection is vital for refactoring and quality improvement. However, existing datasets often frame detection as single-label classification, limiting realism.
objectiveThis paper develops a multi-label dataset for code smell detection, integrating textual features and numerical metrics from open-source Java projects.
methodWe collected code from 103 Java projects, parsed it into Abstract Syntax Trees (ASTs), extracted features, and annotated samples based on prior studies. Data cleaning, unification, and merging techniques were applied to support four code smells: God Class, Data Class, Feature Envy, and Long Method.
resultsThe dataset comprises 107,554 samples with multi-label annotations, improving detection realism. Evaluation shows F1 scores of 95.89% (Data Class), 94.48% (God Class), 88.68% (Feature Envy), and 88.87% (Long Method).
conclusionThis dataset aids advanced studies on code smell detection, particularly for fine-tuning LLMs. Future work can expand it to other languages and additional smells, enhancing diversity and applicability.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.