Journal
JOURNAL OF SYSTEMS AND SOFTWARE
Volume 138, Issue -, Pages 158-173Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jss.2017.12.034
Keywords
Code smells; Software smells; Antipatterns; Software quality; Maintainability; Smell detection tools; Technical debt
Funding
- SENECA project, Marie Sklodowska-Curie Innovative Training Networks (ITN-EID) [642954]
- Marie Curie Actions (MSCA) [642954] Funding Source: Marie Curie Actions (MSCA)
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Context: Smells in software systems impair software quality and make them hard to maintain and evolve. The software engineering community has explored various dimensions concerning smells and produced extensive research related to smells. The plethora of information poses challenges to the community to comprehend the state-of-the-art tools and techniques. Objective: We aim to present the current knowledge related to software smells and identify challenges as well as opportunities in the current practices. Method: We explore the definitions of smells, their causes as well as effects, and their detection mechanisms presented in the current literature. We studied 445 primary studies in detail, synthesized the information, and documented our observations. Results: The study reveals five possible defining characteristics of smells indicator, poor solution, violates best-practices, impacts quality, and recurrence. We curate ten common factors that cause smells to occur including lack of skill or awareness and priority to features over quality. We classify existing smell detection methods into five - groups metrics, rules/heuristics, history, machine learning, and optimization-based detection. Challenges in the smells detection include the tools' proneness to false-positives and poor coverage of smells detectable by existing tools. (C) 2017 Published by Elsevier Inc.
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