TY - GEN
T1 - Stateless Model Checking Under a Reads-Value-From Equivalence
AU - Agarwal, Pratyush
AU - Chatterjee, Krishnendu
AU - Pathak, Shreya
AU - Pavlogiannis, Andreas
AU - Toman, Viktor
PY - 2021
Y1 - 2021
N2 - Stateless model checking (SMC) is one of the standard approaches to the verification of concurrent programs. As scheduling non-determinism creates exponentially large spaces of thread interleavings, SMC attempts to partition this space into equivalence classes and explore only a few representatives from each class. The efficiency of this approach depends on two factors: (a) the coarseness of the partitioning, and (b) the time to generate representatives in each class. For this reason, the search for coarse partitionings that are efficiently explorable is an active research challenge. In this work we present RVF-SMC, a new SMC algorithm that uses a novel reads-value-from (RVF) partitioning. Intuitively, two interleavings are deemed equivalent if they agree on the value obtained in each read event, and read events induce consistent causal orderings between them. The RVF partitioning is provably coarser than recent approaches based on Mazurkiewicz and “reads-from” partitionings. Our experimental evaluation reveals that RVF is quite often a very effective equivalence, as the underlying partitioning is exponentially coarser than other approaches. Moreover, RVF-SMC generates representatives very efficiently, as the reduction in the partitioning is often met with significant speed-ups in the model checking task.
AB - Stateless model checking (SMC) is one of the standard approaches to the verification of concurrent programs. As scheduling non-determinism creates exponentially large spaces of thread interleavings, SMC attempts to partition this space into equivalence classes and explore only a few representatives from each class. The efficiency of this approach depends on two factors: (a) the coarseness of the partitioning, and (b) the time to generate representatives in each class. For this reason, the search for coarse partitionings that are efficiently explorable is an active research challenge. In this work we present RVF-SMC, a new SMC algorithm that uses a novel reads-value-from (RVF) partitioning. Intuitively, two interleavings are deemed equivalent if they agree on the value obtained in each read event, and read events induce consistent causal orderings between them. The RVF partitioning is provably coarser than recent approaches based on Mazurkiewicz and “reads-from” partitionings. Our experimental evaluation reveals that RVF is quite often a very effective equivalence, as the underlying partitioning is exponentially coarser than other approaches. Moreover, RVF-SMC generates representatives very efficiently, as the reduction in the partitioning is often met with significant speed-ups in the model checking task.
UR - http://www.scopus.com/inward/record.url?scp=85113492396&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-81685-8_16
DO - 10.1007/978-3-030-81685-8_16
M3 - Article in proceedings
SN - 978-3-030-81684-1
VL - 1
T3 - Lecture Notes in Computer Science
SP - 341
EP - 366
BT - Computer Aided Verification - 33rd International Conference, CAV 2021, Proceedings
A2 - Silva, Alexandra
A2 - Rustan, K.
A2 - Leino, M.
PB - Springer
CY - Cham
T2 - 33rd International Conference, CAV 2021
Y2 - 20 July 2021 through 23 July 2021
ER -