Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Visualization of test information to assist fault localization
Proceedings of the 24th International Conference on Software Engineering
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
Scalable statistical bug isolation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
SOBER: statistical model-based bug localization
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Empirical Software Engineering
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Formal concept analysis enhances fault localization in software
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Concept lattices in software analysis
Formal Concept Analysis
Healing online service systems via mining historical issue repositories
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
A general noise-reduction framework for fault localization of Java programs
Information and Software Technology
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One time-consuming task in the development of software is debugging. Recent work in fault localization crosschecks traces of correct and failing execution traces, it implicitly searches for association rules which indicate that executing a line will most probably cause the whole execution to fail. This technique has some limitations: it assumes that an error has a single faulty statement origin, and that lines are independent. Our research hypothesis is that using association rules with more expressive premises, some limitations can be alleviated. The solution that we propose combines association rules and formal concept analysis. Our technique is already usable when the size of the execution traces is not too large. We conjecture that the technique can be used to analyze large executions, thanks to the information contained in the Abstract Syntax Tree.