Antipattern and Code Smell False Positives: Preliminary Conceptualization and Classification
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Original versionArcelli Fontana, Dietrich J, Walter B, Yamashita AY, Zanoni M: Antipattern and Code Smell False Positives: Preliminary Conceptualization and Classification. In: Jiu. 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), 2016. IEEE p. 609-613 http://dx.doi.org/10.1109/SANER.2016.84
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 pro- grams, and many of them are found not to be as detrimental to quality as previously conjectured. We are therefore interested in revisiting common anti-patterns and code smells, and building 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 endeavour to develop more pragmatic, context-relevant detection and analysis tools for anti-patterns and code smells.