Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Orthogonal Sampling Formulas: A Unified Approach
SIAM Review
Locating Features in Source Code
IEEE Transactions on Software Engineering
ICSE '81 Proceedings of the 5th international conference on Software engineering
Design and Analysis of Experiments
Design and Analysis of Experiments
Explanation Exploration: Exploring Emergent Behavior
Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation
Applying causal inference to understand emergent behavior
Proceedings of the 40th Conference on Winter Simulation
Program slice distribution functions
Winter Simulation Conference
Mitigating the confounding effects of program dependences for effective fault localization
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
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Unexpected model behaviors need explanation, so valid behaviors can be separated from errors. Understanding unexpected behavior requires accumulation of insight into the behavior and the conditions under which it arises. Explanation Exploration (EE) has been presented to gather insight into unexpected behaviors. EE provides subject matter experts (SMEs) with the capability to test hypotheses about an unexpected behavior by semi-automatically creating conditions of interest under which SMEs can observe the unexpected behavior. EE also reveals the interactions of identified variables that influence the unexpected behavior. Causal Program Slicing, improves EE by: automatically identifying all variables in the model that may influence the unexpected behavior, quantifying how the state changes in those variables influence the unexpected behavior, and mapping the quantified state changes in the variables to the statements in the model’s source code that cause change in state. These capabilities require less SME knowledge and provide more insight than EE.