9 research outputs found
Program analysis : termination proofs for Linear Simple Loops
Termination proof synthesis for simple loops, i.e., loops with only conjoined constraints in the loop guard and variable updates in the loop body, is the building block of termination analysis, as well as liveness analysis, for large complex imperative systems. In particular, we consider a subclass of simple loops which contain only linear constraints in the loop guard and linear updates in the loop body. We call them Linear Simple Loops (LSLs). LSLs are particularly interesting because most loops in practice are indeed linear; more importantly, since we allow the update statements to handle nondeterminism, LSLs are expressive enough to serve as a foundational model for non-linear loops as well. Existing techniques can successfully synthesize a linear ranking function for an LSL if there exists one. When a terminating LSL does not have a linear ranking function, these techniques fail. In this dissertation we describe an automatic method that generates proofs of (universal) termination for LSLs based on the synthesis of disjunctive ranking relations. The method repeatedly finds linear ranking functions on parts of the state space and checks whether the transitive closure of the transition relation is included in the union of the ranking relations. We have implemented the method and have shown experimental evidence of the effectiveness of our method
Proving Termination Starting from the End
We present a novel technique for proving program termination which introduces
a new dimension of modularity. Existing techniques use the program to
incrementally construct a termination proof. While the proof keeps changing,
the program remains the same. Our technique goes a step further. We show how to
use the current partial proof to partition the transition relation into those
behaviors known to be terminating from the current proof, and those whose
status (terminating or not) is not known yet. This partition enables a new and
unexplored dimension of incremental reasoning on the program side. In addition,
we show that our approach naturally applies to conditional termination which
searches for a precondition ensuring termination. We further report on a
prototype implementation that advances the state-of-the-art on the grounds of
termination and conditional termination.Comment: 16 page
On the Termination of Linear and Affine Programs over the Integers
The termination problem for affine programs over the integers was left open
in\cite{Braverman}. For more that a decade, it has been considered and cited as
a challenging open problem. To the best of our knowledge, we present here the
most complete response to this issue: we show that termination for affine
programs over Z is decidable under an assumption holding for almost all affine
programs, except for an extremely small class of zero Lesbegue measure. We use
the notion of asymptotically non-terminating initial variable values} (ANT, for
short) for linear loop programs over Z. Those values are directly associated to
initial variable values for which the corresponding program does not terminate.
We reduce the termination problem of linear affine programs over the integers
to the emptiness check of a specific ANT set of initial variable values. For
this class of linear or affine programs, we prove that the corresponding ANT
set is a semi-linear space and we provide a powerful computational methods
allowing the automatic generation of these sets. Moreover, we are able to
address the conditional termination problem too. In other words, by taking ANT
set complements, we obtain a precise under-approximation of the set of inputs
for which the program does terminate.Comment: arXiv admin note: substantial text overlap with arXiv:1407.455
On Multiphase-Linear Ranking Functions
Multiphase ranking functions () were proposed as a means
to prove the termination of a loop in which the computation progresses through
a number of "phases", and the progress of each phase is described by a
different linear ranking function. Our work provides new insights regarding
such functions for loops described by a conjunction of linear constraints
(single-path loops). We provide a complete polynomial-time solution to the
problem of existence and of synthesis of of bounded depth
(number of phases), when variables range over rational or real numbers; a
complete solution for the (harder) case that variables are integer, with a
matching lower-bound proof, showing that the problem is coNP-complete; and a
new theorem which bounds the number of iterations for loops with
. Surprisingly, the bound is linear, even when the
variables involved change in non-linear way. We also consider a type of
lexicographic ranking functions, , more expressive than types
of lexicographic functions for which complete solutions have been given so far.
We prove that for the above type of loops, lexicographic functions can be
reduced to , and thus the questions of complexity of
detection and synthesis, and of resulting iteration bounds, are also answered
for this class.Comment: typos correcte
Unrestricted Termination and Non-Termination Arguments for Bit-Vector Programs
Proving program termination is typically done by finding a well-founded
ranking function for the program states. Existing termination provers typically
find ranking functions using either linear algebra or templates. As such they
are often restricted to finding linear ranking functions over mathematical
integers. This class of functions is insufficient for proving termination of
many terminating programs, and furthermore a termination argument for a program
operating on mathematical integers does not always lead to a termination
argument for the same program operating on fixed-width machine integers. We
propose a termination analysis able to generate nonlinear, lexicographic
ranking functions and nonlinear recurrence sets that are correct for
fixed-width machine arithmetic and floating-point arithmetic Our technique is
based on a reduction from program \emph{termination} to second-order
\emph{satisfaction}. We provide formulations for termination and
non-termination in a fragment of second-order logic with restricted
quantification which is decidable over finite domains. The resulted technique
is a sound and complete analysis for the termination of finite-state programs
with fixed-width integers and IEEE floating-point arithmetic
Program Synthesis for Program Analysis
In this article, we propose a unified framework for designing static analysers based on program synthesis. For this purpose, we identify a fragment of second-order logic with restricted quantification that is expressive enough to model numerous static analysis problems (e.g., safety proving, bug finding, termination and non-termination proving, refactoring). As our focus is on programs that use bit-vectors, we build a decision procedure for this fragment over finite domains in the form of a program synthesiser. We provide instantiations of our framework for solving a diverse range of program verification tasks such as termination, non-termination, safety and bug finding, superoptimisation, and refactoring. Our experimental results show that our program synthesiser compares positively with specialised tools in each area as well as with general-purpose synthesisers
Program Synthesis for Program Analysis
In this paper, we propose a unified framework for designing static analysers based on program synthesis. For this purpose, we identify a fragment of second-order logic with restricted quantification that is expressive enough to model numerous static analysis problems (e.g., safety proving, bug finding, termination and non-termination proving, refactoring). As our focus is on programs that use bit-vectors, we build a decision procedure for this fragment over finite domains in the form of a program synthesiser. We provide instantiations of our framework for solving a diverse range of program verification tasks such as termination, non-termination, safety and bug finding, superoptimisation and refactoring. Our experimental results show that our program synthesiser
compares positively with specialised tools in each area as well as with general-purpose synthesisers