Identifying utility functions using random forests

Utility functions are general purpose functions, which are useful in many parts of a system. To facilitate reuse, they are usually implemented in specific libraries. However, developers frequently miss opportunities to implement general-purpose functions in utility libraries, which decreases the chances of reuse. In this paper, we describe our ongoing investigation on using Random Forest classifiers to automatically identify utility functions. Using a list of static source code metrics we train a classifier to identify such functions, both in Java (using 84 projects from the Qualitas Corpus) and in JavaScript (using 22 popular projects from GitHub). We achieve the following median results for Java: 0.90 (AUC), 0.83 (precision), 0.88 (recall), and 0.84 (F-measure). For JavaScript, the median results are 0.80 (AUC), 0.75 (precision), 0.89 (recall), and 0.76 (F-measure).