We present an alternative Double Description representation for the domain of NNC (not necessarily closed) polyhedra, together with the corresponding Chernikova-like conversion procedure. The representation uses no slack variable at all and provides a solution to a few technical issues caused by the encoding of an NNC polyhedron as a closed polyhedron in a higher dimension space. We then reconstruct the abstract domain of NNC polyhedra, providing all the operators needed to interface it with commonly available static analysis tools: while doing this, we highlight the efficiency gains enabled by the new representation and we show how the canonicity of the new representation allows for the specification of proper, semantic widening operators. A thorough experimental evaluation shows that our new abstract domain achieves significant efficiency improvements with respect to classical implementations for NNC polyhedra. (C) 2020 Elsevier Inc. All rights reserved.
PPLite: Zero-overhead encoding of NNC polyhedra / Becchi, A; Zaffanella, E. - In: INFORMATION AND COMPUTATION. - ISSN 0890-5401. - 275:(2020), p. 104620. [10.1016/j.ic.2020.104620]
PPLite: Zero-overhead encoding of NNC polyhedra
Becchi, A;
2020-01-01
Abstract
We present an alternative Double Description representation for the domain of NNC (not necessarily closed) polyhedra, together with the corresponding Chernikova-like conversion procedure. The representation uses no slack variable at all and provides a solution to a few technical issues caused by the encoding of an NNC polyhedron as a closed polyhedron in a higher dimension space. We then reconstruct the abstract domain of NNC polyhedra, providing all the operators needed to interface it with commonly available static analysis tools: while doing this, we highlight the efficiency gains enabled by the new representation and we show how the canonicity of the new representation allows for the specification of proper, semantic widening operators. A thorough experimental evaluation shows that our new abstract domain achieves significant efficiency improvements with respect to classical implementations for NNC polyhedra. (C) 2020 Elsevier Inc. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione