Code smells are symptoms of poor design and implementation choices that may hinder code comprehension and possibly increase change- and fault-proneness of source code. Several techniques have been proposed in the literature for detecting code smells. These techniques are generally evaluated by comparing their accuracy on a set of detected candidate code smells against a manually-produced oracle. Unfortunately, such comprehensive sets of annotated code smells are not available in the literature with only few exceptions. For this reason, we created LANDFILL, a Web-based platform for sharing code smell datasets. It already includes a dataset of 243 instances of five types of code smells identified from 20 open source software projects. LANDFILL also provides a set of APIs for programmatically accessing its contents. Anyone can contribute to Landfill by (i) improving existing datasets (e.g., adding missing instances of code smells, flagging possibly incorrectly classified instances), and (ii) sharing and posting new datasets.

Involved researchers: F. Palomba, D. Di Nucci, M. Tufano, G. Bavota, R. Oliveto, D. Poshyvanyk, and A. De Lucia

Context person for the Free University of Bolzano: Gabriele Bavota