Journal article Open Access
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.