Title:
Scalable Parametric Static Analysis
Scalable Parametric Static Analysis
Author(s)
Zhang, Xin
Naik, Mayur
Yang, Hongseok
Naik, Mayur
Yang, Hongseok
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Abstract
Parametric static analysis allows choosing a parameter value to
balance the precision and cost of the instantiated analysis. We
propose an efficient approach to either find a cheapest parameter
value to prove a given query or show that no such parameter
value exists. Our approach is based on refinement, as in CEGAR
(counterexample-guided abstraction refinement), but applies
a novel meta-analysis to abstract counterexample traces to efficiently
find parameter values that are incapable of proving the
query. We formalize our approach in a generic framework and
apply it to two parametric analyses: a thread-escape analysis and
a type-state analysis. The thread-escape analysis is implemented
and applied to eight Java benchmarks comprising 2.5 MLOC. Our
experiments show that our approach is effective in practice: for
our four largest benchmarks, searching 2⁹K[superscript] parameter values for
each of 10K queries on average per benchmark, it finds a cheapest
one for proving 46% queries and shows that none exists for 37%
queries, in one minute per query on average.
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Date Issued
2012
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Text
Resource Subtype
Technical Report