An iterative approach to precondition inference using constrained Horn clauses. Issue 3 (10th August 2018)

• Record Type: Journal Article
• Authors: KAFLE, BISHOKSAN; GALLAGHER, JOHN P.; GANGE, GRAEME; SCHACHTE, PETER; SØNDERGAARD, HARALD; STUCKEY, PETER J.
• Editors: Dal Palu, Alessandro; Tarau, Paul
• Is Part Of: Theory and practice of logic programming. Volume 18:Issue 3/4(2018)
• Page Start: 553
• Page End: 570
• Publication Date: 2018-08-10
• Subjects: Precondition inference, -- backwards analysis, -- abstract interpretation, -- refinement, -- program specialisation, -- program transformation; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068418000091
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: We present a method for automatic inference of conditions on the initial states of a program that guarantee that the safety assertions in the program are not violated. Constrained Horn clauses (CHCs) are used to model the program and assertions in a uniform way, and we use standard abstract interpretations to derive an over-approximation of the set of unsafe initial states. The precondition then is the constraint corresponding to the complement of that set, under-approximating the set of safe initial states. This idea of complementation is not new, but previous attempts to exploit it have suffered from the loss of precision. Here we develop an iterative specialisation algorithm to give more precise, and in some cases optimal safety conditions. The algorithm combines existing transformations, namely constraint specialisation, partial evaluation and a trace elimination transformation. The last two of these transformations perform polyvariant specialisation, leading to disjunctive constraints which improve precision. The algorithm is implemented and tested on a benchmark suite of programs from the literature in precondition inference and software verification competitions.