Adaptive ridge regression system for software cost estimating on multi-collinear datasets
Journal of Systems and Software
Statistical debugging using a hierarchical model of correlated predicates
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
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An important challenge in finding latent errors in software is to find predicates which have the most effect on program failure. Since predicates have mutual effects on each other, it is not a good solution to analyze them in isolation, without considering the simultaneous effects of other predicates on failure. The aim is to detect those predicates which are best bug predictors and meanwhile have the least effects among themselves. To achieve this, Recursive Ridge regression method has been applied.In order to determine the main causes of program failure, the association rule generation is used to detect those predicates which are most often observed with bug predictors in faulty executions. Based on the detected predicates, the faulty paths in control flow graph are introduced to the debugger.Our empirical results on two well-known test suites, EXIF and Siemens imply that the proposed approach could detect main causes of program failure with more accuracy.