A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
New Generation Computing
Model checking and abstraction
ACM Transactions on Programming Languages and Systems (TOPLAS)
Beyond induction variables: detecting and classifying sequences using a demand-driven SSA form
ACM Transactions on Programming Languages and Systems (TOPLAS)
Aspect: detecting bugs with abstract dependences
ACM Transactions on Software Engineering and Methodology (TOSEM)
Model-based diagnosis of hardware designs
Artificial Intelligence
Quickly detecting relevant program invariants
Proceedings of the 22nd international conference on Software engineering
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Model-Based Debugging or How to Diagnose Programs Automatically
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
What went wrong: explaining counterexamples
SPIN'03 Proceedings of the 10th international conference on Model checking software
Better Debugging through More Abstract Observations
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Evaluating Models for Model-Based Debugging
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
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Recent years have seen considerable developments in modeling techniques for automatic fault location in programs. However, much of this research considered the models from a standalone perspective. Instead, this paper focuses on the highly unusual properties of the testing and measurement process, where capabilities differ strongly from the classical hardware diagnosis paradigm. In particular, in an interactive debugging process user interaction may result in highly complex input to improve the process. This work extends the standard entropy-based measurement selection algorithm proposed in (de Kleer and Williams, 1987) to deal with high-level observations about the intended behavior of Java programs, specific to a set of test cases. We show how to incorporate the approach into previously developed model-based debugging frameworks and to how reasoning about high-level properties of programs can improve diagnostic results.