Code smells can be used to capture symptoms of code decay and potential maintenance problems that can be avoided by applying the right refactoring. They can be seen as a source of technical debt. However, tools for code smell detection often provide far too many and different results, and identify many false positive code smell instances. In fact, these tools are rooted on initial and rather informal code smell definitions. This represents a challenge to interpret their results in different ways. In this paper, we provide an Intensity Index, to be used as an estimator to determine the most critical instances, prioritizing the examination of smells and, potentially, their removal. We apply Intensity on the detection of six well known and common smells and we report their Intensity distribution from an analysis performed on 74 systems of the Qualitas Corpus, showing how Intensity could be used to prioritize code smells inspection.

ARCELLI FONTANA, F., Ferme, V., Zanoni, M., Roveda, R. (2015). Towards a prioritization of code debt: A code smell Intensity Index. In Proceedings of the Seventh International Workshop on Managing Technical Debt (MTD 2015) (pp.16-24). Bremen : Institute of Electrical and Electronics Engineers Inc. [10.1109/MTD.2015.7332620].

Towards a prioritization of code debt: A code smell Intensity Index

ARCELLI FONTANA, FRANCESCA
Primo
;
ZANONI, MARCO
;
ROVEDA, RICCARDO
Ultimo
2015

Abstract

Code smells can be used to capture symptoms of code decay and potential maintenance problems that can be avoided by applying the right refactoring. They can be seen as a source of technical debt. However, tools for code smell detection often provide far too many and different results, and identify many false positive code smell instances. In fact, these tools are rooted on initial and rather informal code smell definitions. This represents a challenge to interpret their results in different ways. In this paper, we provide an Intensity Index, to be used as an estimator to determine the most critical instances, prioritizing the examination of smells and, potentially, their removal. We apply Intensity on the detection of six well known and common smells and we report their Intensity distribution from an analysis performed on 74 systems of the Qualitas Corpus, showing how Intensity could be used to prioritize code smells inspection.
paper
Code Debt, Code Smell, Intensity Index, debt, index, software, machine learning, knowledge, software quality, software developing, 74 systems, Qualitas Corpus
English
International Workshop on Managing Technical Debt, MTD co-located with ICSME October 2
2015
Ernst, NA; Avgeriou, P; Kruchten, P
Proceedings of the Seventh International Workshop on Managing Technical Debt (MTD 2015)
9781467373784
2015
2015
16
24
7332620
http://ieeexplore.ieee.org/document/7332620/
reserved
ARCELLI FONTANA, F., Ferme, V., Zanoni, M., Roveda, R. (2015). Towards a prioritization of code debt: A code smell Intensity Index. In Proceedings of the Seventh International Workshop on Managing Technical Debt (MTD 2015) (pp.16-24). Bremen : Institute of Electrical and Electronics Engineers Inc. [10.1109/MTD.2015.7332620].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/91173
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