Previous research demonstrated how code smells (i.e., symptoms of the presence of poor design or implementation choices) threat software maintainability. Moreover, some studies showed that their interaction has a stronger negative impact on the ability of developers to comprehend and enhance the source code when compared to cases when a single code smell instance affects a code element (i.e., a class or a method). While such studies analyzed the effect of the co-presence of more smells from the developers' perspective, a little knowledge regarding which code smell types tend to co-occur in the source code is currently available. Indeed, previous papers on smell co-occurrence have been conducted on a small number of code smell types or on small datasets, thus possibly missing important relationships. To corroborate and possibly enlarge the knowledge on the phenomenon, in this paper we provide a large-scale replication of previous studies, taking into account 13 code smell types on a dataset composed of 395 releases of 30 software systems. Code smell co-occurrences have been captured by using association rule mining, an unsupervised learning technique able to discover frequent relationships in a dataset. The results highlighted some expected relationships, but also shed light on co-occurrences missed by previous research in the field.

Investigating code smell co-occurrences using association rule learning: A replicated study

PALOMBA, FABIO;DE LUCIA, Andrea
2017-01-01

Abstract

Previous research demonstrated how code smells (i.e., symptoms of the presence of poor design or implementation choices) threat software maintainability. Moreover, some studies showed that their interaction has a stronger negative impact on the ability of developers to comprehend and enhance the source code when compared to cases when a single code smell instance affects a code element (i.e., a class or a method). While such studies analyzed the effect of the co-presence of more smells from the developers' perspective, a little knowledge regarding which code smell types tend to co-occur in the source code is currently available. Indeed, previous papers on smell co-occurrence have been conducted on a small number of code smell types or on small datasets, thus possibly missing important relationships. To corroborate and possibly enlarge the knowledge on the phenomenon, in this paper we provide a large-scale replication of previous studies, taking into account 13 code smell types on a dataset composed of 395 releases of 30 software systems. Code smell co-occurrences have been captured by using association rule mining, an unsupervised learning technique able to discover frequent relationships in a dataset. The results highlighted some expected relationships, but also shed light on co-occurrences missed by previous research in the field.
978-1-5090-6597-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4685105
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