Context: Code smells are symptoms of wrong design decisions or coding shortcuts that may
increase defect rate and decrease maintainability. Research on code smells is accelerating …

Context Code smells are patterns in source code associated with an increased defect rate
and a higher maintenance effort than usual, but without a clear definition. Code smells are …

Context Research on code smells accelerates and there are many studies that discuss them
in the machine learning context. However, while data sets used by researchers vary in …

Code smells are structures in code that may indicate maintainability issues. They are
challenging to define, and software engineers detect them differently. Mitigation of this …

Artificial intelligence (AI) has witnessed an exponential increase in use in various
applications. Recently, the academic community started to research and inject new AI-based …

Abstract Context In Empirical Software Engineering, it is crucial to work with representative
samples that reflect the current state of the software industry. An important consideration …

Recent studies concerning open-source community failure show that there is an increasing
need for (semi-) automated support for measuring social, organizational, and socio-technical …

Sharing research data from public funding is an important topic, especially now, during times
of global emergencies like the COVID-19 pandemic, when we need policies that enable …

Software projects are common inputs in Empirical Software Engineering (ESE), and they are
often selected without following a specific strategy, leading to biased samples. To avoid this …

The data set on Zenodo is available using https://doi. org/10.5281/zenodo. 3590101 upon
acceptance of the related data paper. This DOI will always resolve to the newest version of …