A Systematic Literature Review on the Detection of Smells and their Evolution in Object-Oriented and Service-Oriented Systems. Sabir, F., Palma, F., Rasool, G., Gu�h�neuc, Y., & Moha, N. Software: Practice and Experience (JSPE), 49(1):3–39, Wiley, January, 2019. 36 pages.
Paper abstract bibtex This systematic literature review article investigates the key techniques used to identify bad smells in different paradigms of software engineering from object-oriented (OO) to service-oriented (SO). We report, classify, and categorise bad smells and the state-of-the-art detection techniques based on their key characteristics. With this review, we want to identify commonalities and differences in the treatment of bad smells in OO and SO systems. Our research method relies on an automatic search from the relevant digital libraries to find the studies published since January 2000 on bad smells until December 2017. We have conducted a pilot and author-based search to select the 78 most relevant studies after applying inclusion and exclusion criteria. We evaluated the studies based on the bad smells detection techniques and the evolution of different methodologies from OO to SO. We have identified six different studies in which linguistic source code analysis received less attention from the researchers as compared to the static source code analysis. The smells like Yoyo Problem, Un-named Coupling, Intensive Coupling, and Interface Bloat received considerably less attention in the literature. We also identified a catalogue of 30 smells that are infrequently reported for SO systems and require further attention. Moreover, a suite of 20 smells reported for SO systems can also be detected using static source code metrics in OO. Finally, our review highlighted three major research trends that are further sub-divided into 20 research patterns initiating the detection of bad smells towards their correction.
@ARTICLE{Sabir19-JSPE-SLRSmellsOOandSO,
AUTHOR = {Fatima Sabir and Francis Palma and Ghulam Rasool and
Yann-Ga�l Gu�h�neuc and Naouel Moha},
JOURNAL = {Software: Practice and Experience (JSPE)},
TITLE = {A Systematic Literature Review on the Detection of
Smells and their Evolution in Object-Oriented and Service-Oriented
Systems},
YEAR = {2019},
MONTH = {January},
NOTE = {36 pages.},
NUMBER = {1},
PAGES = {3--39},
VOLUME = {49},
EDITOR = {Rajkumar Buyya},
KEYWORDS = {Topic: <b>Code and design smells</b>,
Venue: <b>JSPE</b>},
PUBLISHER = {Wiley},
URL = {http://www.ptidej.net/publications/documents/JSPE19.doc.pdf},
ABSTRACT = {This systematic literature review article investigates
the key techniques used to identify bad smells in different paradigms
of software engineering from object-oriented (OO) to service-oriented
(SO). We report, classify, and categorise bad smells and the
state-of-the-art detection techniques based on their key
characteristics. With this review, we want to identify commonalities
and differences in the treatment of bad smells in OO and SO systems.
Our research method relies on an automatic search from the relevant
digital libraries to find the studies published since January 2000 on
bad smells until December 2017. We have conducted a pilot and
author-based search to select the 78 most relevant studies after
applying inclusion and exclusion criteria. We evaluated the studies
based on the bad smells detection techniques and the evolution of
different methodologies from OO to SO. We have identified six
different studies in which linguistic source code analysis received
less attention from the researchers as compared to the static source
code analysis. The smells like Yoyo Problem, Un-named Coupling,
Intensive Coupling, and Interface Bloat received considerably less
attention in the literature. We also identified a catalogue of 30
smells that are infrequently reported for SO systems and require
further attention. Moreover, a suite of 20 smells reported for SO
systems can also be detected using static source code metrics in OO.
Finally, our review highlighted three major research trends that are
further sub-divided into 20 research patterns initiating the
detection of bad smells towards their correction.}
}
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The smells like Yoyo Problem, Un-named Coupling, Intensive Coupling, and Interface Bloat received considerably less attention in the literature. We also identified a catalogue of 30 smells that are infrequently reported for SO systems and require further attention. Moreover, a suite of 20 smells reported for SO systems can also be detected using static source code metrics in OO. 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We report, classify, and categorise bad smells and the \r\n state-of-the-art detection techniques based on their key \r\n characteristics. With this review, we want to identify commonalities \r\n and differences in the treatment of bad smells in OO and SO systems. \r\n Our research method relies on an automatic search from the relevant \r\n digital libraries to find the studies published since January 2000 on \r\n bad smells until December 2017. We have conducted a pilot and \r\n author-based search to select the 78 most relevant studies after \r\n applying inclusion and exclusion criteria. We evaluated the studies \r\n based on the bad smells detection techniques and the evolution of \r\n different methodologies from OO to SO. We have identified six \r\n different studies in which linguistic source code analysis received \r\n less attention from the researchers as compared to the static source \r\n code analysis. The smells like Yoyo Problem, Un-named Coupling, \r\n Intensive Coupling, and Interface Bloat received considerably less \r\n attention in the literature. We also identified a catalogue of 30 \r\n smells that are infrequently reported for SO systems and require \r\n further attention. 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