A Bayesian Approach for the Detection of Code and Design Smells

The presence of code and design smells can have a severe impact on the quality of a program. Consequently, their detection and correction have drawn the attention of both researchers and practitioners who have proposed various approaches to detect code and design smells in programs. However, none of these approaches handle the inherent uncertainty of the detection process. We propose a Bayesian approach to manage this uncertainty. First, we present a systematic process to convert existing state-of-the-art detection rules into a probabilistic model. We illustrate this process by generating a model to detect occurrences of the Blob antipattern. Second, we present results of the validation of the model: we built this model on two

• Type: Conference
• Year: 2009
• Where: QSIC
• Authors: Foutse Khomh, Stéphane Vaucher, Yann-Gaël Guéhéneuc, Houari A. Sahraoui
• Keywords: Design Smells, Detection, QSIC 2009, Software Engineering, State-of-the-art Detection Rules