Tristan Denmat
ABSTRACT
The current trend in debugging and testing is to cross-check information collected during several executions. Jones et al., for example, propose to use the instruction coverage of passing and failing runs in order to visualize suspicious statements. This seems promising but lacks a formal justification. In this paper, we show that the method of Jones et al. can be re-interpreted as a data mining procedure. More particularly, they define an indicator which characterizes association rules between data. With this formal framework we are able to explain intrinsic limitations of the above indicator.
- R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of ACM SIGMOD Int. Conf. on Management of Data, pages 207--216. ACM Press, 1993. Google Scholar
Digital Library
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. Int. Conf. Very Large Data Bases, VLDB, pages 487--499, 1994. Google ScholarDigital Library
- S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In Proc. ACM SIGMOD Int. conf. on Management of Data, pages 255--264. ACM Press, 1997. Google ScholarDigital Library
- T. Denmat, M. Ducassé, and O. Ridoux. Data mining and cross-checking of execution traces. Are-interpretation of Jones, Harrold and Stasko test information visualization (Long version). Research Report RR-5661, INRIA, August 2005. http://www.inria.fr/rrrt/rr-5661.html.Google Scholar
- J. A. Jones, M. J. Harrold, and J. Stasko. Visualization of test information to assist fault localization. In Proc. Int. Conf. on Software Engineering, pages 467--477. ACM Press, 2002. Google ScholarDigital Library
Index Terms
- Data mining and cross-checking of execution traces: a re-interpretation of Jones, Harrold and Stasko test information
Recommendations
On mining cross-graph quasi-cliques
KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data miningJoint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in ...
Mining frequent cross-graph quasi-cliques
Joint mining of multiple datasets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in bioinformatics, jointly mining multiple gene expression datasets obtained by different ...
Mining uncertain data
As an important data mining and knowledge discovery task, association rule mining searches for implicit, previously unknown, and potentially useful pieces of information—in the form of rules revealing associative relationships—that are embedded in the ...
Comments