Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Sanjit A. Seshia. Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis. Technical Report UCB/EECS-2011-68, EECS Department, University of California, Berkeley, 2011.

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Abstract

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems.

This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed.

BibTeX

@techreport{seshia-tr11,
    Author = {Seshia, Sanjit A.},
    Title = {Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2011},
    Month = {May},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2011/EECS-2011-68.html},
    Number = {UCB/EECS-2011-68},
    Abstract = {Even with impressive advances in automated formal methods, certain problems in system verification
and synthesis remain challenging. Examples include the verification of quantitative properties of software
involving constraints on timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling, incompleteness in specifications, and
the complexity of underlying decision problems.
This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive
inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general
rules or concepts to conclusions about specific problem instances, includes techniques such as logical
inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a
concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction
constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive
and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is
used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of
loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also
discussed.},
}