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The presence of antipatterns can have a negative impact on the quality of a program. Consequently, their efficient detection has drawn the attention of both researchers and practitioners. However, most aspects of antipatterns are loosely specified because quality assessment is ultimately a human-centric process that requires contextual data. Consequently, there is always a degree of uncertainty on whether a class in a program is an antipattern or not. None of the existing automatic detection approaches handle the inherent uncertainty of the detection process. First, we present BDTEX (Bayesian Detection Expert), a Goal Question Metric (GQM) based approach to build Bayesian Belief Networks (BBNs) from the definitions of antipatterns. We discuss the advantages of BBNs over rule-based models and illustrate BDTEX on the Blob antipattern. Second, we validate BDTEX with three antipatterns: Blob, Functional …