Speaker: Hila Cohen

Title: A Probabilistic Framework for Estimating Log Completeness with regard to Dynamic Specification Mining Algorithms

Abstract:

Dynamic specification mining extracts candidate specifications from logs of execution

traces. Existing algorithms differ in the kinds of traces they take as input and in the

kinds of candidate specification they present as output. The more traces we see, the more

we may be confident that the extracted specifications faithfully characterize the program

under investigation. However, producing and analyzing traces is costly, so how would we

know we have seen enough traces? And, how would we know we have not wasted resources

and seen too many of them?

We address these important questions by presenting a novel, black box,

probabilistic framework based on a notion of log completeness, and by applying it to

three different well known specification mining algorithms from the literature: k-Tails,

Synoptic, and mining of scenario-based triggers and effects. Extensive evaluation shows

the soundness, generalizability, and usefulness of the framework and its contribution to the

state-of-the-art in dynamic specification mining.