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Journal of Systems and Software; ZENODO
Article . Other literature type . 2018 . Peer-reviewed
License: Elsevier TDM
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Journal of Systems and Software
Article
License: CC BY
Data sources: UnpayWall
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a survey on software smells

Authors: Sharma,Tushar; Spinellis, Diomidis;

a survey on software smells

Abstract

Abstract 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.

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    174
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
174
Top 1%
Top 1%
Top 1%
Green
hybrid