A Bayesian approach for software quality prediction

Many statistical algorithms have been proposed for software quality prediction of fault-prone and non fault-prone program modules. The main goal of these algorithms is the improvement of software development processes. In this paper, we introduce a new software prediction algorithm. Our approach is purely Bayesian and is based on finite Dirichlet mixture models. The implementation of the Bayesian approach is done through the use of the Gibbs sampler. Experimental results are presented using simulated data, and a real application for software modules classification is also included.

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