5 research outputs found
Intensional and Extensional Semantics of Bounded and Unbounded Nondeterminism
We give extensional and intensional characterizations of nondeterministic
functional programs: as structure preserving functions between biorders, and as
nondeterministic sequential algorithms on ordered concrete data structures
which compute them. A fundamental result establishes that the extensional and
intensional representations of non-deterministic programs are equivalent, by
showing how to construct a unique sequential algorithm which computes a given
monotone and stable function, and describing the conditions on sequential
algorithms which correspond to continuity with respect to each order.
We illustrate by defining may and must-testing denotational semantics for a
sequential functional language with bounded and unbounded choice operators. We
prove that these are computationally adequate, despite the non-continuity of
the must-testing semantics of unbounded nondeterminism. In the bounded case, we
prove that our continuous models are fully abstract with respect to may and
must-testing by identifying a simple universal type, which may also form the
basis for models of the untyped lambda-calculus. In the unbounded case we
observe that our model contains computable functions which are not denoted by
terms, by identifying a further "weak continuity" property of the definable
elements, and use this to establish that it is not fully abstract
Step-Indexed Relational Reasoning for Countable Nondeterminism
Programming languages with countable nondeterministic choice are
computationally interesting since countable nondeterminism arises when modeling
fairness for concurrent systems. Because countable choice introduces
non-continuous behaviour, it is well-known that developing semantic models for
programming languages with countable nondeterminism is challenging. We present
a step-indexed logical relations model of a higher-order functional programming
language with countable nondeterminism and demonstrate how it can be used to
reason about contextually defined may- and must-equivalence. In earlier
step-indexed models, the indices have been drawn from {\omega}. Here the
step-indexed relations for must-equivalence are indexed over an ordinal greater
than {\omega}
Compositional software verification based on game semantics
One of the major challenges in computer science is to put programming on a firmer mathematical basis, in order to improve the correctness of computer programs. Automatic program verification is acknowledged to be a very hard problem, but current work is reaching the point where at least the foundational�· aspects of the problem can be addressed and it is becoming a part of industrial software development. This thesis presents a semantic framework for verifying safety properties of open sequ;ptial programs. The presentation is focused on an Algol-like programming language that embodies many of the core ingredients of imperative and functional languages and incorporates data abstraction in its syntax. Game semantics is used to obtain a compositional, incremental way of generating accurate models of programs. Model-checking is made possible by giving certain kinds of concrete automata-theoretic representations of the model. A data-abstraction refinement procedure is developed for model-checking safety properties of programs with infinite integer types. The procedure starts by model-checking the most abstract version of the program. If no counterexample, or a genuine one, is found, the procedure terminates. Otherwise, it uses a spurious counterexample to refine the abstraction for the next iteration. Abstraction refinement, assume-guarantee reasoning and the L* algorithm for learning regular languages are combined to yield a procedure for compositional verification. Construction of a global model is avoided using assume-guarantee reasoning and the L* algorithm, by learning assumptions for arbitrary subprograms. An implementation based on the FDR model checker for the CSP process algebra demonstrates practicality of the methods
Compositional software verification based on game semantics
One of the major challenges in computer science is to put programming on a firmer mathematical basis, in order to improve the correctness of computer programs. Automatic program verification is acknowledged to be a very hard problem, but current work is reaching the point where at least the foundational�· aspects of the problem can be addressed and it is becoming a part of industrial software development. This thesis presents a semantic framework for verifying safety properties of open sequ;ptial programs. The presentation is focused on an Algol-like programming language that embodies many of the core ingredients of imperative and functional languages and incorporates data abstraction in its syntax. Game semantics is used to obtain a compositional, incremental way of generating accurate models of programs. Model-checking is made possible by giving certain kinds of concrete automata-theoretic representations of the model. A data-abstraction refinement procedure is developed for model-checking safety properties of programs with infinite integer types. The procedure starts by model-checking the most abstract version of the program. If no counterexample, or a genuine one, is found, the procedure terminates. Otherwise, it uses a spurious counterexample to refine the abstraction for the next iteration. Abstraction refinement, assume-guarantee reasoning and the L* algorithm for learning regular languages are combined to yield a procedure for compositional verification. Construction of a global model is avoided using assume-guarantee reasoning and the L* algorithm, by learning assumptions for arbitrary subprograms. An implementation based on the FDR model checker for the CSP process algebra demonstrates practicality of the methods.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (Great Britain) (EPSRC)Overseas Research Students Award Scheme (ORSAS)GBUnited Kingdo