Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
A correction to the algorithm in Reiter's theory of diagnosis
Artificial Intelligence
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Aspect: detecting bugs with abstract dependences
ACM Transactions on Software Engineering and Methodology (TOSEM)
Model-based diagnosis of hardware designs
Artificial Intelligence
Programmers use slices when debugging
Communications of the ACM
On the relationship between model-based debugging and program slicing
Artificial Intelligence
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Using abstract dependences to localize faults from procedural programs
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
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Locating faults is one of the most time consuming tasks in today's fast paced economy. Testing and formal verification techniques like model-checking are usually used for detecting faults but do not attempt to locate the root-cause for the detected faulty behavior. This article makes use of an abstract dependences between program variables for detecting and locating faults in alias-free programs in cases where an abstract specification is available. The idea of using dependences for fault detection and localization is not new. But the relationship between the abstract model and the concrete evaluation of programs have not been considered so far. In particular we show that the dependence model is correct. Whenever the dependence model reveals a fault there is a test case, which also reveals a fault.