Application of Clustering Algorithms for Discovering Bug Patterns in JavaScript Software

Applications developed with JavaScript language are increasing every day, not only for client-side, but also for server-side and for mobile devices. In this context, the existence of tools to identify faults is fundamental in order to assist developers during the evolution of their applications. Different tools and approaches have been proposed over the years, however they have limitations to evolve over time, becoming obsolete quickly. The reason for this is the use of a fixed list of pre-defined faults that are searched in the code. The BugAID tool implements a semiautomatic strategy for discovering bug patterns by grouping the changes made during the project development. The objective of this work is to contribute to the BugAID tool, extending this tool with improvements in the extraction of characteristics to be used by the clustering algorithm. The extended module of the BugAID extraction module (BE) that extracts the characteristics is called BE+. Additionally, an evaluation of the clustering algorithms used for discovering fault patterns in JavaScript software is performed. The results show that the DBScan and Optics algorithms with BE+ presented the best results for the Rand, Jaccard and Adjusted Rand indexes, while HDBScan with BE and BE+ presented the worst result.