MongoDB is one of the most popular NoSQL database engines. To foster scalability, it provides multiple features such as schemaless data storage or sharding. However, those new features introduce additional considerations for the maintainer to be careful, which might lead to erroneous implementation choices often referred to as code smells or antipatterns. Detecting and fixing those code smells can play a crucial role for developers in their maintenance efforts. We present SMEAGOL (SMEll and Antipattern detection for monGOdb appLications), a static analysis tool to detect MongoDB code smells in JavaScript applications. SMEAGOL relies on CodeQL and detects code smells by analyzing and extracting all the necessary information (e.g., data structure) from the database access code of the application. We demonstrate it by examining the evolution of MongoDB code smells in five popular open-source projects, showing promising results. Video link: https://youtu.be/h4Xbp9dIFtO Repository link: https://github.com/bocherry/SMEAGOL_tool

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.

The increasing data volume and the variety of data formats of modern data-intensive systems unveiled the boundaries of traditional relational database management systems. NoSQL technologies aim to fulfill shortcomings through numerous features such as allowing unstructured, schema-less data storage. However, new features also pose challenges to software engineering techniques that used to work well for relational databases. In this paper, we present an approach to retrieve database accesses in JavaScript applications that use MongoDB. The approach handles JavaScript's highly dynamic and typeless nature through heuristics to avoid collision with third-party libraries. The aim is to identify the part of the source code involved in the database communication as the first step towards additional static analysis approaches. We evaluated the approach on an oracle of 307 open-source projects and reached a precision of 78%. We demonstrate potential use cases of the approach through case studies on the evolution of open-source systems.