Abstract
By combining algorithmic learning, decision procedures, predicate abstraction, and simple templates, we present an automated technique for finding quantified loop invariants. Our technique can find arbitrary first-order invariants (modulo a fixed set of atomic propositions and an underlying SMT solver) in the form of the given template and exploits the flexibility in invariants by a simple randomized mechanism. The proposed technique is able to find quantified invariants for loops from the Linux source, as well as for the benchmark code used in the previous works. Our contribution is a simpler technique than the previous works yet with a reasonable derivation power.
This work was supported by the Engineering Research Center of Excellence Program of Korea Ministry of Education, Science and Technology(MEST) / National Research Foundation of Korea(NRF) (Grant 2010-0001717). This work was partly supported by MoE Tier-2 grant R-252-000-411-112 and by the National Science Council of Taiwan projects No. NSC97-2221-E-001-003-MY3, NSC97-2221-E-001-006-MY3, the FORMES Project within LIAMA Consortium, and the French ANR project SIVES ANR-08-BLAN-0326-01.
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Kong, S., Jung, Y., David, C., Wang, BY., Yi, K. (2010). Automatically Inferring Quantified Loop Invariants by Algorithmic Learning from Simple Templates. In: Ueda, K. (eds) Programming Languages and Systems. APLAS 2010. Lecture Notes in Computer Science, vol 6461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17164-2_23
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