A theory of diagnosis from first principles
Artificial Intelligence
A correction to the algorithm in Reiter's theory of diagnosis
Artificial Intelligence
Model checking
Simplifying failure-inducing input
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
An axiomatic basis for computer programming
Communications of the ACM
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
On the relationship between model-based debugging and program slicing
Artificial Intelligence
Isolating cause-effect chains from computer programs
Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering
From symptom to cause: localizing errors in counterexample traces
POPL '03 Proceedings of the 30th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Program Slicing Using Weakest Preconditions
FME '96 Proceedings of the Third International Symposium of Formal Methods Europe on Industrial Benefit and Advances in Formal Methods
What went wrong: explaining counterexamples
SPIN'03 Proceedings of the 10th international conference on Model checking software
Automated Fault Localization for C Programs
Electronic Notes in Theoretical Computer Science (ENTCS)
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Detecting and localizing a fault within a program is a non-trivial and time consuming task. Most of the efforts spent for automating the task have focused on fault detection. In this paper we shift the focus on fault localization. We introduce a resolution calculus that allows for representing the program's behavior based on correctness assumptions. The fault localization task is reduced to finding consistent assumptions which are represented as a non-monotonic reasoning process where efficient algorithms exist. Finally, we compare our approach with a previous approach to fault localization that is based on trace analysis. As a result we can show that our approach is less sensitive to search assumptions.