Anti-patterns and code smells are archetypes used for describing software design shortcomings that can negatively affect software quality, in particular maintainability. Tools, metrics and methodologies have been developed to identify these archetypes, based on the assumption that they can point at problematic code. However, recent empirical studies have shown that some of these archetypes are ubiquitous in real world programs, and many of them are found to not be as detrimental to quality as previously conjectured. We are therefore interested on revisiting common anti-patterns and code smells, and build a catalogue of cases that constitute candidates for “false positives”. We propose a preliminary classification of such false positives with the aim of facilitating a better understanding of the effects of anti-patterns and code smells in practice. We hope that the development and further refinement of such a classification can support researchers and tool vendors in their endeavor to develop more pragmatic, context-relevant detection and analysis tools for anti-patterns and code smells.

Additional Metadata
ISBN 978-1-5090-1855-0
Persistent URL dx.doi.org/ 10.1109/SANER.2016.84
Conference IEEE International Conference on Software Analysis, Evolution, and Reengineering
Citation
F.A. Fontana (Francesca Arcelli), J. Dietrich (Jens), B. Walter (Bartosz), Yamashita, A, & M. Zanoni (Marco). (2016). Antipattern and Code Smell False Positives: Preliminary Conceptualization and Classification. In Proceedings of the 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) (pp. 609–613). doi: 10.1109/SANER.2016.84