DECOR: A Method for the Specification and Detection of Code and Design Smells

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When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) (2017, positive): “In our study we used the thresholds suggested in the paper by Moha et al. [51].”
On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation (2017, methodology): “Indeed, DECOR identifies only one of the actual instances, while JDeodorant and HIST detect three of them.”
CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2023, methodology): “Rule/Heuristic-based approaches rely on rules and heuristic criteria set by experienced developers or experts (Moha et al., 2010; Sharma et al., 2016).”
An Exploratory Study of the Impact of Code Smells on Software Change-proneness (2009, methodology): “[31] reported that the DECOR current detection algorithms for antipatterns ensure 100% recall and have a precision greater than 31% in the worst case, with an average greater than 60%.”
Comparing and experimenting machine learning techniques for code smell detection (2015, methodology): “For example, DÉCOR (Moha et al 2010) requires that the rules are specified in the form of domain specific language and this specification process must be undertaken by domain experts, engineers or quality experts.”