Software Code Smells and Defects: An Empirical Investigation

Reuben Brown, Des Greer

2023

Abstract

Code smells indicate weaknesses in software design that may slow down development or increase the risk of bugs or failures in the future. This paper aims to investigate the correlation of code smells with defects within classes. The method used uses a tool to automatically detect code smells in selected projects and then assesses the correlation of these to the number of defects found in the code. Most existing articles determine that software modules/classes with more smells tend to have more defects. However, while the experiments in this paper covering a range of languages agreed with this, the correlation was found to be weak. There remains a need for further investigation of the types of code smells that tend to indicate or predict defects occurring. Future work will perform more detailed experiments by investigating a larger quantity and variety of software systems as well as more granular studies into types of code smell and defects arising.

Paper Citation

in Harvard Style

Brown R. and Greer D. (2023). Software Code Smells and Defects: An Empirical Investigation. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-647-7, SciTePress, pages 570-580. DOI: 10.5220/0011974500003464

in Bibtex Style

@conference{enase23,
author={Reuben Brown and Des Greer},
title={Software Code Smells and Defects: An Empirical Investigation},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2023},
pages={570-580},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011974500003464},
isbn={978-989-758-647-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Software Code Smells and Defects: An Empirical Investigation
SN - 978-989-758-647-7
AU - Brown R.
AU - Greer D.
PY - 2023
SP - 570
EP - 580
DO - 10.5220/0011974500003464
PB - SciTePress