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Are Code Smell Detection Tools Suitable For Detecting Architecture Degradation?
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. Karlstad University. (Software Engineering Research Group)ORCID iD: 0000-0002-0107-2108
(Al Quassim University)
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (Software Engineering Research Group)ORCID iD: 0000-0003-1777-884X
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (Software Engineering Research Group)ORCID iD: 0000-0002-3180-9182
2017 (English)Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017.
Keyword [en]
architecture erosion, code smells, data mining, case study
National Category
Software Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-63784DOI: 10.1145/3129790.3129808ISBN: 978-1-4503-5217-8 (electronic)OAI: oai:DiVA.org:kau-63784DiVA: diva2:1142115
Conference
4th Workshop on Software Architecture Erosion and Architectural Consistency (SAEroCon 2017) co-located with the 11th European Conference on Software Architecture (ECSA 2017)
Available from: 2017-09-18 Created: 2017-09-18 Last updated: 2018-01-13

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Publisher's full texthttp://dl.acm.org/citation.cfm?id=3129808&CFID=810685213&CFTOKEN=14879230

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CiteExportLink to record
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