Domain-specific tailoring of code smells: an empirical study

Authors:
Yuepu Guo;Carolyn Seaman;Nico Zazworka;Forrest Shull
Affiliations:
University of Maryland Baltimore County, Baltimore, Maryland;University of Maryland Baltimore County, Baltimore, Maryland;Fraunhofer Center for Experimental Software Engineering, Maryland;Fraunhofer Center for Experimental Software Engineering, Maryland
Venue:
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2
Year:
2010

Citing 5
Cited 3

The WyCash portfolio management system

OOPSLA '92 Addendum to the proceedings on Object-oriented programming systems, languages, and applications (Addendum)
Refactoring: improving the design of existing code

Refactoring: improving the design of existing code
Metrics and Laws of Software Evolution - The Nineties View

METRICS '97 Proceedings of the 4th International Symposium on Software Metrics
Object-Oriented Metrics in Practice

Object-Oriented Metrics in Practice
The evolution and impact of code smells: A case study of two open source systems

ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement

A multiple case study of design pattern decay, grime, and rot in evolving software systems

Software Quality Control
A case study on effectively identifying technical debt

Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering
To what extent can maintenance problems be predicted by code smell detection? - An empirical study

Information and Software Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Code smells refer to commonly occurring patterns in source code that indicate poor programming practices or code decay. Detecting code smells helps developers find design problems that can cause trouble in future maintenance. Detection rules for code smells, based on software metrics, have been proposed, but they do not take domain-specific characteristics into consideration. In this study we investigate whether such generic heuristics can be tailored to include domain-specific factors. Input into these domain-specific heuristics comes from an iterative empirical field study in a software maintenance project. The results yield valuable insight into code smell detection.