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Article Citation - WoS: 13Citation - Scopus: 14On Mean Residual Life of Discrete Time Multi-State Systems(Nctu-national Chiao Tung Univ Press, 2013) Eryilmaz, SerkanThe mean residual life function is an important characteristic in reliability and survival analysis. Although many papers have studied the mean residual life of binary systems, the study of this characteristic for multi-state systems is new. In this paper, we study mean residual life of discrete time multi-state systems that have M + 1 states of working efficiency. In particular, we consider two different definitions of mean residual life function and evaluate them assuming that the degradation in multi-state system follows a Markov process.Article Citation - WoS: 8Citation - Scopus: 13Joint Reliability Importance in a Binary k-out-of- n: G System With Exchangeable Dependent Components(Nctu-national Chiao Tung Univ Press, 2014) Mahmoud, Boushaba; Eryilmaz, SerkanIn this paper, we study joint reliability importance (JRI) in a k -out-of- n : G structure consisting of exchangeable dependent coimponents. We obtain a closed-form formula for the JRI of multiple components of a k -out-of- n : G system with dependent components. We illustrate the results for the k -out-of- n: G model under stress-strength setup. The results extend and generalize the results in the literature from various perspectives including exchangeable type dependence for the JRI of two components.Article Citation - WoS: 28Citation - Scopus: 28Experience in Predicting Fault-Prone Software Modules Using Complexity Metrics(Nctu-national Chiao Tung Univ Press, 2012) Yu, Liguo; Mishra, AlokComplexity metrics have been intensively studied in predicting fault-prone software modules. However, little work is done in studying how to effectively use the complexity metrics and the prediction models under realistic conditions. In this paper, we present a study showing how to utilize the prediction models generated from existing projects to improve the fault detection on other projects. The binary logistic regression method is used in studying publicly available data of five commercial products. Our study shows (1) models generated using more datasets can improve the prediction accuracy but not the recall rate; (2) lowering the cut-off value can improve the recall rate, but the number of false positives will be increased, which will result in higher maintenance effort. We further suggest that in order to improve model prediction efficiency, the selection of source datasets and the determination of cut-Off values should be based on specific properties of a project. So far, there are no general rules that have been found and reported to follow
