A Multivocal Mapping Study of MongoDB Smells

Code smells are symptoms of poor design or bad implementation choices. Their automatic detection is helpful for various reasons. For example, the detected smells can guide developers during code inspection to find the causes of maintenance problems. Many code smells have been proposed for several technologies, including database communication, such as ORM or SQL antipatterns. However, despite its popularity, no research has been conducted on MongoDB smells. We present a systematic multivocal literature mapping study, also covering “grey” literature, to build a catalog of MongoDB code smells. After evaluating 1,498 artifacts (e.g., blog posts, online articles, book chapters, scientific papers, presentation slides, and videos) from 12 search engines, we manually reviewed 174 sources and devised a catalog of 76 smells organized into 11 categories. We present the catalog of MongoDB code smells through a series of examples.