2016
Control Explicit-Data Symbolic Model Checking
BAUCH, Petr, Vojtěch HAVEL a Jiří BARNATZákladní údaje
Originální název
Control Explicit-Data Symbolic Model Checking
Autoři
BAUCH, Petr (203 Česká republika, domácí), Vojtěch HAVEL (203 Česká republika, domácí) a Jiří BARNAT (203 Česká republika, garant, domácí)
Vydání
ACM Transactions on Software Engineering and Methodology, 2016, 1049-331X
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 2.516
Kód RIV
RIV/00216224:14330/16:00088090
Organizační jednotka
Fakulta informatiky
UT WoS
000377289000005
Klíčová slova anglicky
formal methods; model checking; static analysis; modular arithmetic
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 23. 8. 2016 11:04, prof. RNDr. Jiří Barnat, Ph.D.
Anotace
V originále
Automatic verification of programs and computer systems with data nondeterminism (e.g., reading from user input) represents a significant and well-motivated challenge. The case of parallel programs is especially difficult, because then also the control flow nontrivially complicates the verification process. We apply the techniques of explicit-state model checking to account for the control aspects of a program to be verified and use set-based reduction of the data flow, thus handling the two sources of nondeterminism separately. We build the theory of set-based reduction using first-order formulae in the bit-vector theory to encode the sets of variable evaluations representing program data. These representations are tested for emptiness and equality (state matching) during the verification, and we harness modern satisfiability modulo theory solvers to implement these tests. We design two methods of implementing the state matching, one using quantifiers and one that is quantifier-free, and we provide both analytical and experimental comparisons. Further experiments evaluate the efficiency of the set-based reduction method, showing the classical, explicit approach to fail to scale with the size of data domains. Finally, we propose and evaluate two heuristics to decrease the number of expensive satisfiability queries, together yielding a 10-fold speedup.
Návaznosti
GA15-08772S, projekt VaV |
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