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Are Code Smell Detection Tools Suitable For Detecting Architecture Degradation?
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). Karlstad University. (Software Engineering Research Group)ORCID-id: 0000-0002-0107-2108
(Al Quassim University)
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). Karlstads universitet, Fakulteten för ekonomi, kommunikation och IT, Avdelningen för datavetenskap. (Software Engineering Research Group)ORCID-id: 0000-0003-1777-884X
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (Software Engineering Research Group)ORCID-id: 0000-0002-3180-9182
2017 (engelsk)Inngår i: ECSA '17 Proceedings of the 11th European Conference on Software Architecture: Companion Proceedings, Association for Computing Machinery (ACM), 2017, s. 138-144Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Context: Several studies suggest that there is a relation between code smells and architecture degradation. They claim that classes, which have degraded architecture-wise, can be detected on the basis of code smells, at least if these are manually identiÿed in the source code.

Objective: To evaluate the suitability of contemporary code smell detection tools by combining different smell categories for ÿnding classes that show symptoms of architecture degradation.

Method: A case study is performed in which architectural in-consistencies in an open source system are detected via reflexion modeling and code smell metrics are collected through several tools. Using data mining techniques, we investigate if it is possible to auto-matically and accurately classify classes connected to architectural inconsistencies based on the gathered code smell data.

Results: Results suggest that existing code smell detection techniques, as implemented in contemporary tools, are not sufficiently accurate for classifying whether a class contains architectural in-consistencies, even when combining categories of code smells.

Conclusion: It seems that current automated code smell detection techniques require ÿne-tuning for a speciÿc system if they are to be used for ÿnding classes with architectural inconsistencies. More research on architecture violation causes is needed to build more accurate detection techniques that work out-of-the-box.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2017. s. 138-144
Emneord [en]
architecture erosion, code smells, data mining, case study
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-63784DOI: 10.1145/3129790.3129808ISI: 000426556400032ISBN: 978-1-4503-5217-8 (digital)OAI: oai:DiVA.org:kau-63784DiVA, id: diva2:1142115
Konferanse
4th Workshop on Software Architecture Erosion and Architectural Consistency (SAEroCon 2017) co-located with the 11th European Conference on Software Architecture (ECSA 2017)
Tilgjengelig fra: 2017-09-18 Laget: 2017-09-18 Sist oppdatert: 2018-11-16bibliografisk kontrollert

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Forlagets fullteksthttp://dl.acm.org/citation.cfm?id=3129808&CFID=810685213&CFTOKEN=14879230

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Lenhard, JörgBlom, MartinHerold, Sebastian

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