Context-based code smells prioritization for prefactoring

To find opportunities for applying prefactoring, several techniques for detecting bad smells in source code have been proposed. Existing smell detectors are often unsuitable for developers who have a specific context because these detectors do not consider their current context and output the results that are mixed with both smells that are and are not related to such context. Consequently, the developers must spend a considerable amount of time identifying relevant smells. As described in this paper, we propose a technique to prioritize bad code smells using developers' context. The explicit data of the context are obtained using a list of issues extracted from an issue tracking system. We applied impact analysis to the list of issues and used the results to specify which smells are associated with the context. Consequently, our approach can provide developers with a list of prioritized bad code smells related to their current context. Several evaluations using open source projects demonstrate the effectiveness of our technique.