A survey of exploratory software development
The Computer Journal - Special issue on methodologies (systems and software)
Debugging with dynamic slicing and backtracking
Software—Practice & Experience
Delta algorithms: an empirical analysis
ACM Transactions on Software Engineering and Methodology (TOSEM)
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Isolating cause-effect chains from computer programs
Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering
ConSIT: A Conditioned Program Slicer
ICSM '00 Proceedings of the International Conference on Software Maintenance (ICSM'00)
An Empirical Study of the Effect of Semantic Differences on Programmer Comprehension
IWPC '02 Proceedings of the 10th International Workshop on Program Comprehension
Cost effective dynamic program slicing
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Theories, Methods and Tools in Program Comprehension: Past, Present and Future
IWPC '05 Proceedings of the 13th International Workshop on Program Comprehension
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
Experimental program analysis: a new program analysis paradigm
Proceedings of the 2006 international symposium on Software testing and analysis
PADS '09 Proceedings of the 2009 ACM/IEEE/SCS 23rd Workshop on Principles of Advanced and Distributed Simulation
| Hi-index | 0.00 |
Unexpected behaviors in simulations require explanation, so that decision makers and subject matter experts can separate valid behaviors from design or coding errors. Validation of unexpected behaviors requires accumulation of insight into the behavior and the conditions under which it arises. Stochastic simulations are known for unexpected behaviors that can be difficult to recreate and explain. To facilitate exploration, analysis and understanding of unexpected behaviors in stochastic simulations we have developed a novel approach, called Program Slice Distribution Functions (PSDFs), for quantifying the uncertainty of the dynamic program slices (simulation executions) causing unexpected behaviors. Our use of PSDFs is the first approach to quantifying the uncertainty in program slices for stochastic simulations and extends the state of the art in analysis and informed decision making based on simulation outcomes. We apply PSDFs to a published epidemic simulation and describe how users can apply PSDFs to their own stochastic simulations.