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Macedo, Charles Mendes de. Application of clustering algorithms for discovering bug patterns in JavaScript software. 2019. 82 p. Dissertation (Master of Science) – School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 2019. 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. Most of these tools use a list of predefined faults that are discovered from the observation of the programming best practices and developer intuition. To improve these tools, the automatic discovery of faults and code smells is important because it allows to identify which ones actually occur in practice and frequently. A tool that implements a semiautomatic strategy for discovering bug patterns by grouping the changes made during the project development is the BugAID. 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 that extracts the characteristics is called BE+. Additionally, an evaluation of the clustering algorithms used for discovering fault patterns in JavaScript software is performed.

Aplicação de algoritmos de agrupamento para descoberta de padrões de defeito em software JavaScript