A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
PELAS-Program Error-Locating Assistant System
IEEE Transactions on Software Engineering
A correction to the algorithm in Reiter's theory of diagnosis
Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
Structure and chance: melding logic and probability for software debugging
Communications of the ACM
Aspect: detecting bugs with abstract dependences
ACM Transactions on Software Engineering and Methodology (TOSEM)
Model-based diagnosis of hardware designs
Artificial Intelligence
New directions in debugging hardware designs
IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
Programmers use slices when debugging
Communications of the ACM
On the relationship between model-based debugging and program slicing
Artificial Intelligence
Algorithmic Program DeBugging
VHDL: Analysis and Modeling of Digital Systems
VHDL: Analysis and Modeling of Digital Systems
Debugging Hardware Designs Using a Value-Based Model
Applied Intelligence
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Logic programs for consistency-based diagnosis
Logic programs for consistency-based diagnosis
Diagnosing tree-decomposable circuits
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Error traces in model-based debugging of hardware description languages
Proceedings of the sixth international symposium on Automated analysis-driven debugging
Model-Based Debugging -- State of the Art And Future Challenges
Electronic Notes in Theoretical Computer Science (ENTCS)
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
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Debugging is a time-consuming task. This holds especially for large programs that are usually written by different groups of programmers. An example for this observation is the hardware design domain. Nowadays hardware designs are written in special hardware description language, e.g., VHDL, by groups which work in different companies and places. Moreover, there is a high pressure for completing the system in time with a very high quality because of the huge costs for correcting a bug after manufacturing the circuit. In order to decrease time for debugging we introduce an approach for diagnosis of VHDL hardware designs and present first empirical results. In contrast to other debugging approaches we make use of model-based diagnosis which is a general diagnosis approach. The models we use are logical descriptions of the syntax and semantics of a VHDL program that can be automatically derived from the program at compile time. The main part of this paper describes a general model and the derivation of specialized models that capture only some aspects of the program. The specialized models should be used in a specific debugging situation where they deliver the most appropriate solution in reasonable time. In order to select such a model we propose the use of a probability-based selection strategy. For example, larger programs should be debugged using a model only distinguishing concurrent VHDL statements and not sequential statements. As a result of multi-model reasoning in this domain we expect performance gains allowing to debug larger designs in a reasonable time, and more expressive diagnosis results.