  Researchers apply machine-learning techniques for code smell detection to
counter the subjectivity of many code smells. Such approaches need a large,
manually annotated dataset for training and benchmarking. Existing literature
offers a few datasets; however, they are small in size and, more importantly,
do not focus on the subjective code snippets. In this paper, we present DACOS,
a manually annotated dataset containing 10,267 annotations for 5,192 code
snippets. The dataset targets three kinds of code smells at different
granularity: multifaceted abstraction, complex method, and long parameter list.
The dataset is created in two phases. The first phase helps us identify the
code snippets that are potentially subjective by determining the thresholds of
metrics used to detect a smell. The second phase collects annotations for
potentially subjective snippets. We also offer an extended dataset DACOSX that
includes definitely benign and definitely smelly snippets by using the
thresholds identified in the first phase. We have developed TagMan, a web
application to help annotators view and mark the snippets one-by-one and record
the provided annotations. We make the datasets and the web application
accessible publicly. This dataset will help researchers working on smell
detection techniques to build relevant and context-aware machine-learning
models.
