Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models and comparing the results of the new model against the baseline model. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also evaluate the actual gain provided by the intensity index with respect to the other metrics in the model, including the ones used to compute the code smell intensity. We observe that the intensity index is much more important as compared to other metrics used for predicting the buggyness of smelly classes.

Palomba, F., Zanoni, M., Arcelli Fontana, F., De Lucia, A., Oliveto, R. (2017). Smells like teen spirit: Improving bug prediction performance using the intensity of code smells. In Proceedings of the 32nd International Conference on Software Maintenance and Evolution (ICSME 2016) (pp.244-255). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSME.2016.27].

Smells like teen spirit: Improving bug prediction performance using the intensity of code smells

Zanoni, M
;
Arcelli Fontana, F
;
2017

Abstract

Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models and comparing the results of the new model against the baseline model. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also evaluate the actual gain provided by the intensity index with respect to the other metrics in the model, including the ones used to compute the code smell intensity. We observe that the intensity index is much more important as compared to other metrics used for predicting the buggyness of smelly classes.
No
paper
code smells; bug prediction; code smell intensity; structural metrics; process metrics
English
International Conference on Software Maintenance and Evolution (ICSME) october 2-10
9781509038060
Palomba, F., Zanoni, M., Arcelli Fontana, F., De Lucia, A., Oliveto, R. (2017). Smells like teen spirit: Improving bug prediction performance using the intensity of code smells. In Proceedings of the 32nd International Conference on Software Maintenance and Evolution (ICSME 2016) (pp.244-255). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSME.2016.27].
Palomba, F; Zanoni, M; Arcelli Fontana, F; De Lucia, A; Oliveto, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/137823
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