Dr. Rong-Tsorng Wang (王榮琮)

Assistant Professor

Department of Statistics

Tunghai University



1.      Rong-TSorng Wang (201?), “Reliability Evaluation Techniques”, a chapter of “Energy Efficient Fault Tolerant Systems”, Editors (Prof Dhiraj K Pradhan / Dr Jimson Mathew / Dr Rishad Shafik, University of Bristol)

2.      Rong-Tsorng Wang (2012), “A dependent model for fault tolerant software systems during debugging”, IEEE Transactions on Reliability, Vol.61, No.2, pp.504-515.

3.      Rong-Tsorng Wang (2007), “A reliability model for multivariate exponential distributions”, Journal of Multivariate Analysis, Vol.98, No.5, pp.1033-1042.

4.      R. Wang (2005), “A mixture and self-exciting model for software reliability”, Statistics and Probability Letters, Vol.72, No.3, pp.187-194.



1.      Rong-Tsorng Wang (2012) A stochastic model for N-version programming system during debugging
NSC 101-2118-M-029 -001
2.      Rong-Tsorng Wang (2010) Bivariate distributions on counting processes
NSC 99-2118-M-029 -007
3.      Rong-Tsorng Wang (2009) Properties of bivariate exponential models and their applications
NSC 98-2118-M-029 -002
4.   Rong-Tsorng Wang (2008) On Martingale Approach to Software Reliability
      NSC 97-2118-M-029 -007
5.   Rong-Tsorng Wang (2007)  Modeling Software Failure Occurrences with Aftereffects.
      NSC 96-2118-M-029-005
6.   Rong-Tsorng Wang (2006)  Bivariate Counting Process for Software Reliability with Imperfect Elimination and Propagation of Faults.
      NSC 95-2118-M-029-003
7.   Rong-Tsorng Wang (2005) Properties of a Self-exciting Point Process with Mixture.
      NSC 94-2118-M-029-007.
8.   Rong-Tsorng Wang (2003)  On Imperfect Debugging for Software Reliability with Optimal Release Time.
      NSC 92-2118-M-029-006
9.   Rong-Tsorng Wang (2002)  Reliability Models with Dependence Between Components.
      NSC 91-2118-M-029-008



1.  Stochastic Processes and Mathematical Statistics

2.  Reliability Theory and Reliability Modeling

3.  Software Reliability Modeling and Fault-Tolerance



 Stochastic Processes, Mathematical Statistics, Survey Sampling, Linear Algebra, Probability, Statistics