Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Evaluation of safety-critical software
Communications of the ACM
Compound-Poisson Software Reliability Model
IEEE Transactions on Software Engineering
Faults on its sleeve: amplifying software reliability testing
ISSTA '93 Proceedings of the 1993 ACM SIGSOFT international symposium on Software testing and analysis
A Markov Chain Model for Statistical Software Testing
IEEE Transactions on Software Engineering
Some Conservative Stopping Rules for the Operational Testing of Safety-Critical Software
IEEE Transactions on Software Engineering
A Binary Markov Process Model for Random Testing
IEEE Transactions on Software Engineering
Systems testing and statistical test data coverage
COMPSAC '97 Proceedings of the 21st International Computer Software and Applications Conference
Efficient Verification of Behavioral Models Using Sequential Sampling Technique
VLSI '99 Proceedings of the IFIP TC10/WG10.5 Tenth International Conference on Very Large Scale Integration: Systems on a Chip
Achieving the Quality of Verification for Behavioral Models with Minimum Effort
ISQED '00 Proceedings of the 1st International Symposium on Quality of Electronic Design
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Verification of complex behavioral models has become a critical and time-consuming process. Determine when to switch to different testing strategy phase is key to improving efficiency. This paper presents an overview of the existing statistical stopping rules that can be used for behavioral models verification. We examined the stopping rules using two VHDL models for five consecutive test phases. The results of the coverage gained and the number of testing patterns applied are then compared for each stopping rule. We conclude that the confidence-based stopping criterion out-performs others in terms of efficiency.