Context‐based approach to prioritize code smells for prefactoring. Issue 6 (14th September 2017)

• Record Type: Journal Article
• Authors: Sae‐Lim, Natthawute; Hayashi, Shinpei; Saeki, Motoshi
• Other Names: Bavota Gabriele guestEditor.; Maletic Jonathan I. guestEditor.; Collard Michael L. guestEditor.
• Is Part Of: Journal of software. Volume 30:Issue 6(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-09-14
• Subjects: code smell -- impact analysis -- issue tracking system -- prefactoring; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1886
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract : Existing techniques for detecting code smells (indicators of source code problems) do not consider the current context, which renders them unsuitable for developers who have a specific context, such as modules within their focus. Consequently, the developers must spend time identifying relevant smells. We propose a technique to prioritize code smells using the developers' context. 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 the context‐relevant smells. Results show that our approach can provide developers with a list of prioritized code smells related to their current context. We conducted several empirical studies to investigate the characteristics of our technique and factors that might affect the ranking quality. Additionally, we conducted a controlled experiment with professional developers to evaluate our technique. The results demonstrate the effectiveness of our technique. Abstract : In this paper, we propose a technique to prioritize code smells using the developers' context. We applied impact analysis to a list of issues in issue tracking system and used the results to specify the context‐relevant smells. Results show that our approach can provide developers with a list of prioritized code smells related to their current context. This paper also presents empirical studies and a controlled experiment related to the proposed technique.