A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered an evolutionary-based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter, the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem: This leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA.
Software quality is one of important field in software engineering that related to software satisfaction. Some models and methods have made to calculating quality. Almost all of these models use quality factors or metrics, but calculate them is another problem because we can not calculate them exactly or can not determine some of them in some software project and usually our data about quality factors and metrics are incomplete or uncertain. Also these models can not predict software quality before calculate all of quality metrics and factors. Bayesian Networks have become a popular tool for modeling many kinds of statistical problems over the last decade. In this paper we proposed a model for software quality with BNs and ISO9126 quality model. Also by this model we can reduce time and cost of calculating software quality.