Abstract
JavaScript has become one of the widely-used languages. However, as the size of JavaScript-based applications grows, the number of defects grows as well. Recent studies have produced a set of manually defined rules to identify these defects. We propose, in this work, the automation of deriving these rules to ensure scalability and potentially the detection of a wider set of defects without requiring any extensive knowledge on rules tuning. To this end, we rely on a base of existing code smells that is used to train the detection rules using Genetic Programming and find the best threshold of metrics composing the rules. The evaluation of our work on 9 JavaScript web projects has shown promising results in terms of detection precision of 92% and recall of 85%, with no threshold tuning required.
Ian Shoenberger
Software Engineer
Mohamed Wiem Mkaouer
Assistant Professor of Software Engineering
Research interests software refactoring and quality.
