Program verification and discovery using probabilistic...

Details Program verification and discovery using probabilistic... Program verification and discovery using probabilistic...

2007-01-12

2010-06-01

Vincent, David R (Department: 2129)

Data processing: artificial intelligence

Machine learning

C717S106000

Reexamination Certificate

active

07729999

ABSTRACT:
In one embodiment, a computer system performs a method for verifying the validity or invalidity of a software routine by learning appropriate invariants at each program point. A computer system chooses an abstract domain that is sufficiently precise to express the appropriate invariants. The computer system associates an inconsistency measure with any two abstract elements of the abstract domain. The computer system searches for a set of local invariants configured to optimize a total inconsistency measure which includes a sum of local inconsistency measures. The computer system optimizes the total inconsistency measure for all input/output pairs of the software routine. In one embodiment, the optimization of total inconsistency is achieved by the computer system which repeatedly replaces a locally inconsistent invariant with a new invariant, randomly selected among the possible invariants which are locally less inconsistent with the current invariants at the neighboring program points.

REFERENCES:
patent: 2006/0236305 (2006-10-01), Chang et al.
“Ten Years of Hoare's Logic: A Survey—Part I”, ACM Transactions on Programming Languages and Systems (TOPLAS), vol. 3, Issue 4 (Oct. 1981), pp. 431-483.
“A verification framework for agent programming with declarative goals”, by de Boer et al, Journal of Applied Logic, 2005, pp. 277-302.

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