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M i (t) = predicted maintainability of the RVS machine subsystems.
(M_{{{text{r}}. {text{v}}. {text{s}}}} (t)) = predicted maintainability of the RVS machine.
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Although there are very few studies which use search-based techniques for predicting maintainability and change proneness, we found that the results of the application of search-based techniques for effort estimation and defect prediction are encouraging.
This paper also focuses on the effectiveness of feature reduction techniques such as rough set analysis (RSA) and principal component analysis (PCA) when they are applied for predicting maintainability.
In this paper, three artificial intelligence techniques (AI) such as hybrid approach of functional link artificial neural network (FLANN) with genetic algorithm (GA), particle swarm optimization (PSO) and clonal selection algorithm (CSA), i.e., FLANN-Genetic (FGA and AFGA), FLANN-PSO (FPSO and MFPSO), FLANN-CSA (FCSA) are applied to design a model for predicting maintainability.
An outcome of this empirical study is a set of statistical models for predicting the maintainability of XSL transformations with relatively high accuracy.
An acceleration test is conducted with the specimens of actual dimensions and material to predict the maintainability of the structural and functional integrity until the end of the fuel life.
The predicted probabilities of mortality and maintainability required for risk-adjustment purposes were estimated using the logistic regression model, and the predictive power of the model was measured using the c-statistics.
Our results show that model implementations with higher degrees of framework invasiveness also had structural characteristics which previously have been shown to predict poor maintainability, a non-functional code quality attribute of concern.
Analysts predicted revenue of $373 million.
(in predicted order of finish).
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