Probability, statistics, and queueing theory with computer science applications
Probability, statistics, and queueing theory with computer science applications
A Markov Chain Model for Statistical Software Testing
IEEE Transactions on Software Engineering
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Likelilood ratio gradient estimation: an overview
WSC '87 Proceedings of the 19th conference on Winter simulation
Optimization via adaptive sampling and regenerative simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Modern Information Retrieval
Simulation Modeling and Analysis
Simulation Modeling and Analysis
A Pragmatic Survey of Automated Debugging
AADEBUG '93 Proceedings of the First International Workshop on Automated and Algorithmic Debugging
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Dynamic slicing on Java bytecode traces
ACM Transactions on Programming Languages and Systems (TOPLAS)
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability
Kaveri: delivering the indus java program slicer to eclipse
FASE'05 Proceedings of the 8th international conference, held as part of the joint European Conference on Theory and Practice of Software conference on Fundamental Approaches to Software Engineering
IEEE Transactions on Software Engineering
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Stochastic simulations frequently exhibit behaviors that are difficult to recreate and analyze, owing largely to the stochastics themselves, and consequent program dependency chains that can defy human reasoning capabilities. We present a novel approach called Markov Chain Execution Traces (MCETs) for efficiently representing sampled stochastic simulation execution traces and ultimately driving semi-automated analysis methods that require accurate, efficiently generated candidate execution traces. The MCET approach is evaluated, using new and established measures, against both additional novel and existing approaches for computing dynamic program slices in stochastic simulations. MCET's superior performance is established. Finally, a description of how users can apply MCETs to their own stochastic simulations and a discussion of the new analyses MCETs can enable are presented.