The relationship between traceable code patterns and code smells

Zadia Codabux, Kazi Zakia Sultana, Byron J. Williams

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Context: It is important to maintain software quality as a software system evolves. Managing code smells in source code contributes towards quality software. While metrics have been used to pinpoint code smells in source code, we present an empirical study on the correlation of code smells with class-level (micro pattern) and methodlevel (nano-pattern) traceable patterns of code. Objective: This study explores the relationship between code smells and class-level and method-level structural code constructs. Method: We extracted micro patterns at the class level and nano-patterns at the method level from three versions of Apache Tomcat and PersonalBlog and Roller from Standford SecuriBench and compared their distributions in code smell versus non-code smell classes and methods.Result: We found that DataManager, Record and Outline micro patterns are more frequent in classes having code smell compared to non-code smell classes in the applications we analyzed. localReader, localWriter, Switcher, and ArrReader nano-patterns are more frequent in code smell methods compared to the non-code smell methods. Conclusion: We conclude that code smells are correlated with both micro and nano-patterns.

Original languageEnglish
Title of host publicationProceedings - SEKE 2017
Subtitle of host publication29th International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages444-449
Number of pages6
ISBN (Electronic)1891706411
DOIs
StatePublished - 2017
Event29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017 - Pittsburgh, United States
Duration: 5 Jul 20177 Jul 2017

Publication series

NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

Conference

Conference29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017
Country/TerritoryUnited States
CityPittsburgh
Period5/07/177/07/17

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