copilottheorem: kinduction for Copilot.
Some tools to prove properties on Copilot programs with kinduction model checking.
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Dependencies  ansiterminal (>=0.8 && <0.10), base (>=4.9 && <5), bimap (==0.3.*), containers (>=0.4 && <0.7), copilotcore (==3.1.*), datadefault (==0.7.*), directory (==1.3.*), mtl (>=2.0 && <2.3), parsec (>=2.0 && <3.2), pretty (>=1.0 && <1.2), process (==1.6.*), random (==1.1.*), transformers (==0.5.*), xml (==1.3.*) [details] 
License  BSD3Clause 
Author  Jonathan Laurent 
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[back to package description]Copilot Theorem
Highly automated proof techniques are a necessary step for the widespread adoption of formal methods in the software industry. Moreover, it could provide a partial answer to one of its main issue which is scalability.
copilottheorem is a Copilot library aimed at checking automatically some safety properties on Copilot programs. It includes:
A general interface for provers and a proof scheme mechanism aimed at splitting the task of proving a complex property into checking a sequence of smaller lemmas.
A prover implementing basic kinduction model checking [1], useful for proving basic kinductive properties and for pedagogical purposes.
A prover producing native inputs for the Kind2 model checker, developed at University of Iowa. The latter uses both the kinduction algorithm extended with path compression and structural abstraction [2] and the IC3 algorithm with counterexample generalization based on approximate quantifier elimination [3].
A Tutorial
Installation instructions
copilottheorem needs the following dependencies to be installed:
 The copilotcore and copilotlanguage Haskell libraries
 The Yices2 SMTsolver:
yicessmt2
must be in your$PATH
 The Z3 SMTsolver:
z3
must be in your$PATH
 The Kind2 model checker:
kind2
must be in your$PATH
To build it, just clone this repository and use cabal install
. You will find
some examples in the examples
folder, which can be built with cabal install
too, producing an executable copilottheoremexample
in your .cabal/bin
folder.
First steps
copilottheorem is aimed at checking safety properties on Copilot programs. Intuitively, a safety property is a property which express the idea that nothing bad can happen. In particular, any invalid safety property can be disproved by a finite execution trace of the program called a counterexample. Safety properties are often opposed to liveness properties, which express the idea that something good will eventually happen. The latters are out of the scope of copilottheorem.
Safety properties are simply expressed with standard boolean streams. In
addition to triggers and observers declarations, it is possible to bind a
boolean stream to a property name with the prop
construct in the
specification.
For instance, here is a straightforward specification declaring one property:
spec :: Spec
spec = do
prop "gt0" (x > 0)
where
x = [1] ++ (1 + x)
Let's say we want to check that gt0
holds. For this, we use the prove ::
Prover > ProofScheme > Spec > IO ()
function exported by Copilot.Theorem
.
This function takes three arguments:
 The prover we want to use. For now, two provers are available, exported by
the
Copilot.Theorem.Light
andCopilot.Theorem.Kind2
module.  A proof scheme, which is a sequence of instructions like check, assume, assert...
 The Copilot specification
Here, we can just write
prove (lightProver def) (check "gt0") spec
where lightProver def
stands for the light prover with default
configuration.
The Prover interface
The Copilot.Theorem.Prover
defines a general interface for provers. Therefore,
it is really easy to add a new prover by creating a new object of type
Prover
. The latter is defined like this:
data Cex = Cex
type Infos = [String]
data Output = Output Status Infos
data Status
= Valid
 Invalid (Maybe Cex)
 Unknown
 Error
data Feature = GiveCex  HandleAssumptions
data Prover = forall r . Prover
{ proverName :: String
, hasFeature :: Feature > Bool
, startProver :: Core.Spec > IO r
, askProver :: r > [PropId] > PropId > IO Output
, closeProver :: r > IO ()
}
Each prover mostly has to provide a askProver
function which takes as an
argument
 The prover descriptor
 A list of assumptions
 A conclusion
and checks if the assumptions logically entail the conclusion.
Two provers are provided by default: Light
and Kind2
.
The light prover
The light prover is a really simple prover which uses the Yices SMT solver
with the QF_UFLIA
theory and is limited to prove kinductive properties,
that is properties such that there exists some k such that:
 The property holds during the first k steps of the algorithm.
 From the hypothesis the property has held during k consecutive steps, we can prove it is still true one step further.
For instance, in this example
spec :: Spec
spec = do
prop "gt0" (x > 0)
prop "neq0" (x /= 0)
where
x = [1] ++ (1 + x)
the property "gt0"
is inductive (1inductive) but the property "neq0"
is
not.
The light prover is defined in Copilot.Theorem.Light
. This module exports the
lightProver :: Options > Prover
function which builds a prover from a record
of type Options
:
data Options = Options
{ kTimeout :: Integer
, onlyBmc :: Bool
, debugMode :: Bool }
Here,
kTimeout
is the maximum number of steps of the kinduction algorithm the prover executes before giving up. If
onlyBmc
is set toTrue
, the prover will only search for counterexamples and won't try to prove the properties discharged to it.  If
debugMode
is set toTrue
, the SMTLib queries produced by the prover are displayed in the standard output.
Options
is an instance of the Data.Default
typeclass:
instance Default Options where
def = Options
{ kTimeout = 100
, debugMode = False
, onlyBmc = False }
Therefore, def
stands for the default configuration.
The Kind2 prover
The Kind2 prover uses the model checker with the same name, from Iowa
university. It translates the Copilot specification into a modular transition
system (the Kind2 native format) and then calls the kind2
executable.
It is provided by the Copilot.Theorem.Kind2
module, which exports a kind2Prover
:: Options > Prover
where the Options
type is defined as
data Options = Options { bmcMax :: Int }
and where bmcMax
corresponds to the bmc_max
option of kind2 and is
equivalent to the kTimeout
option of the light prover. Its default value is
0, which stands for infinity.
Combining provers
The combine :: Prover > Prover > Prover
function lets you merge two provers
A and B into a prover C which launches both A and B and returns the most
precise output. It would be interesting to implement other merging behaviours
in the future. For instance, a lazy one such that C launches B only if A has
returns unknown or error.
As an example, the following prover is used in Driver.hs
:
prover =
lightProver def {onlyBmc = True, kTimeout = 5}
`combine` kind2Prover def
We will discuss the internals and the experimental results of these provers later.
Proof schemes
Let's consider again this example:
spec :: Spec
spec = do
prop "gt0" (x > 0)
prop "neq0" (x /= 0)
where
x = [1] ++ (1 + x)
and let's say we want to prove "neq0"
. Currently, the two available solvers
fail at showing this noninductive property (we will discuss this limitation
later). Therefore, we can prove the more general inductive lemma "gt0"
and
deduce our main goal from this. For this, we use the proof scheme
assert "gt0" >> check "neq0"
instead of just check "neq0"
. A proof scheme is chain of primitives schemes
glued by the >>
operator. For now, the available primitives are:
check "prop"
checks whether or not a given property is true in the current context.assume "prop"
adds an assumption in the current context.assert "prop"
is a shortcut forcheck "prop" >> assume "prop"
.assuming :: [PropId] > ProofScheme > ProofScheme
is such thatassuming props scheme
assumes the list of properties props, executes the proof scheme scheme in this context, and forgets the assumptions.msg "..."
displays a string in the standard output
We will discuss the limitations of this tool and a way to use it in practice later.
Some examples
Some examples are in the examples folder. The Driver.hs
contains the main
function to run any example. Each other example file exports a specification
spec
and a proof scheme scheme
. You can change the example being run just
by changing one import directive in Driver.hs
.
These examples include:
Incr.hs
: a straightforward example in the style of the previous one.Grey.hs
: an example where two different implementations of a periodical counter are shown to be equivalent.BoyerMoore.hs
: a certified version of the majority vote algorithm introduced in the Copilot tutorial.SerialBoyerMoore.hs
: a serial version of the first step of the Boyer Moore algorithm, where a new element is added to the list and the majority candidate is updated at each clock tick. See the section Limitations related to the SMT solvers for an analysis of this example.
Technical details
An introduction to SMTbased model checking
An introduction to the modelchecking techniques used by copilottheorem can be
found in the doc
folder of this repository. It consists in a self sufficient
set of slides. You can find some additional readings in the References
section.
Architecture of copilottheorem
An overview of the proving process
Each prover first translates the Copilot specification into an intermediate representation best suited for model checking. Two representations are available:
The IL format: a Copilot program is translated into a list of quantifierfree equations over integer sequences, implicitly universally quantified by a free variable n. Each sequence roughly corresponds to a stream. This format is the one used in G. Hagen's thesis [4]. The light prover works with this format.
The TransSys format: a Copilot program is flattened and translated into a state transition system [1]. Moreover, in order to keep some structure in this representation, the variables of this system are grouped by nodes, each node exporting and importing variables. The Kind2 prover uses this format, which can be easily translated into the native format.
For each of these formats, there is a folder in src/Copilot/Theorem
which
contains at least
Spec.hs
where the format is definedPrettyPrint.hs
for pretty printing (useful for debugging)Translate.hs
where the translation process fromCore.Spec
is defined.
These three formats share a simplified set of types and operators, defined
respectively in Misc.Type
and Misc.Operator
.
An example
The following program:
spec = do
prop "pos" (fib > 0)
where
fib :: Stream Word64
fib = [1, 1] ++ (fib + drop 1 fib)
can be translated into this IL specification:
SEQUENCES
s0 : Int
MODEL INIT
s0[0] = 1
s0[1] = 1
MODEL REC
s0[n + 2] = s0[n] + s0[n + 1]
PROPERTIES
'pos' : s0[n] > 0
or this modular transition system:
NODE 's0' DEPENDS ON []
DEFINES
out : Int =
1 > pre out.1
out.1 : Int =
1 > pre out.2
out.2 : Int =
(out) + (out.1)
NODE 'proppos' DEPENDS ON [s0]
IMPORTS
(s0 : out) as 's0.out'
(s0 : out.1) as 's0.out.1'
(s0 : out.2) as 's0.out.2'
DEFINES
out : Bool =
(s0.out) > (0)
NODE 'top' DEPENDS ON [proppos, s0]
IMPORTS
(proppos : out) as 'pos'
(s0 : out) as 's0.out'
(s0 : out.1) as 's0.out.1'
(s0 : out.2) as 's0.out.2'
PROPS
'pos' is (top : pos)
Note that the names of the streams are lost in the Copilot reification process [7] and so we have no way to keep them.
Types
In these three formats, GADTs are used to statically ensure a part of the
typecorectness of the specification, in the same spirit it is done in the
other Copilot libraries. copilottheorem handles only three types which are
Integer
, Real
and Bool
and which are handled by the SMTLib standard.
copilottheorem works with pure reals and integers. Thus, it is unsafe in the
sense it ignores integer overflow problems and the loss of precision due to
floating point arithmetic.
The rules of translation between Copilot types and copilottheorem types are
defined in Misc/Cast
.
Operators
The operators provided by Misc.Operator
mostly consists in boolean
connectors, linear operators, equality and inequality operators. If other
operators are used in the Copilot program, they are handled using
nondeterminism or uninterpreted functions.
The file CoreUtils/Operators
contains helper functions to translate Copilot
operators into copilottheorem operators.
The Light prover
As said in the tutorial, the light prover is a simple tool implementing the
basic kinduction algorithm [1]. The Light
directory contains three files:
Prover.hs
: the prover and the kinduction algorithm are implemented in this file.SMT.hs
contains some functions to interact with the Yices SMT provers.SMTLib.hs
is a set of functions to output SMTLib directives. It uses theMisc.SExpr
module to deal with Sexpressions.
The code is both concise and simple and should be worth a look.
The prover first translates the copilot specification into the IL format.
This translation is implemented in IL.Translate
. It is straightforward as the
IL format does not differ a lot from the copilot core format. This is the
case because the reification process has transformed the copilot program such
that the ++
operator only occurs at the top of a stream definition.
Therefore, each stream definition directly gives us a recurrence equation and
initial conditions for the associated sequence.
The translation process mostly:
 converts the types and operators, using uninterpreted functions to handle nonlinear operators and external functions.
 creates a sequence for each stream, local stream ands external stream.
The reader is invited to use the light prover on the examples with debugMode
= true
, in order to have a look at the SMTLib code produced. For instance, if
we check the property "pos"
on the previous example involving the Fibonacci
sequence, we get:
<step> (setlogic QF_UFLIA)
<step> (declarefun n () Int)
<step> (declarefun s0 (Int) Int)
<step> (assert (= (s0 (+ n 2)) (+ (s0 (+ n 0)) (s0 (+ n 1)))))
<step> (assert (= (s0 (+ n 3)) (+ (s0 (+ n 1)) (s0 (+ n 2)))))
<step> (assert (> (s0 (+ n 0)) 0))
<step> (push 1)
<step> (assert (or false (not (> (s0 (+ n 1)) 0))))
<step> (checksat)
<step> (pop 1)
<step> (assert (= (s0 (+ n 4)) (+ (s0 (+ n 2)) (s0 (+ n 3)))))
<step> (assert (> (s0 (+ n 1)) 0))
<step> (push 1)
<step> (assert (or false (not (> (s0 (+ n 2)) 0))))
<step> (checksat)
unsat
<step> (pop 1)
Here, we just kept the outputs related to the <step>
psolver, which is the
solver trying to prove the continuation step.
You can see that the SMT solver is used in an incremental way (push
and pop
instructions), so we don't need to restart it at each step of the algorithm
(see [2]).
The Kind2 prover
The Kind2 prover first translates the copilot specification into a modular
transition system. Then, a chain of transformations is applied to this system
(for instance, in order to remove dependency cycles among nodes). After this,
the system is translated into the Kind2 native format and the kind2
executable is launched. The following sections will bring more details about
this process.
Modular transition systems
Let's look at the definition of a modular transition systems, in the
TransSys.Spec
module:
type NodeId = String
type PropId = String
data Spec = Spec
{ specNodes :: [Node]
, specTopNodeId :: NodeId
, specProps :: Map PropId ExtVar }
data Node = Node
{ nodeId :: NodeId
, nodeDependencies :: [NodeId]
, nodeLocalVars :: Map Var LVarDescr
, nodeImportedVars :: Bimap Var ExtVar
, nodeConstrs :: [Expr Bool] }
data Var = Var {varName :: String}
deriving (Eq, Show, Ord)
data ExtVar = ExtVar {extVarNode :: NodeId, extVarLocalPart :: Var }
deriving (Eq, Ord)
data VarDescr = forall t . VarDescr
{ varType :: Type t
, varDef :: VarDef t }
data VarDef t =
Pre t Var
 Expr (Expr t)
 Constrs [Expr Bool]
data Expr t where
Const :: Type t > t > Expr t
Ite :: Type t > Expr Bool > Expr t > Expr t > Expr t
Op1 :: Type t > Op1 x t > Expr x > Expr t
Op2 :: Type t > Op2 x y t > Expr x > Expr y > Expr t
VarE :: Type t > Var > Expr t
A transition system (Spec
type) is mostly made of a list of nodes. A node
is just a set of variables living in a local namespace and corresponding to the
Var
type. The ExtVar
type is used to identify a variable in the global
namespace by specifying both a node name and a variable. A node contains two
types of variables:
Some variables imported from other nodes. The structure
nodeImportedVars
binds each imported variable to its local name. The set of nodes from which a node imports some variables is stored in thenodeDependencies
field.Some locally defined variables contained in the
nodeLocalVars
field. Such a variable can be Defined as the previous value of another variable (
Pre
constructor ofVarDef
)  Defined by an expression involving other variables (
Expr
constructor)  Defined implicitly by a set of constraints (
Constrs
constructor)
 Defined as the previous value of another variable (
The translation process
First, a copilot specification is translated into a modular transition system.
This process is defined in the TransSys.Translate
module. Each stream is
associated to a node. The most significant task of this translation process is
to flatten the copilot specification so the value of all streams at time n
only depends on the values of all the streams at time n  1, which is not the
case in the Fib
example shown earlier. This is done by a simple program
transformation which turns this:
fib = [1, 1] ++ (fib + drop 1 fib)
into this:
fib0 = [1] ++ fib1
fib1 = [1] ++ (fib1 + fib0)
and then into the node
NODE 'fib' DEPENDS ON []
DEFINES
out : Int =
1 > pre out.1
out.1 : Int =
1 > pre out.2
out.2 : Int =
(out) + (out.1)
Once again, this flattening process is made easier by the fact that the ++
operator only occurs leftmost in a stream definition after the reification
process.
Some transformations over modular transition systems
The transition system obtained by the TransSys.Translate
module is perfectly
consistent. However, it can't be directly translated into the Kind2 native
file format. Indeed, it is natural to bind each node to a predicate but the
Kind2 file format requires that each predicate only uses previously defined
predicates. However, some nodes in our transition system could be mutually
recursive. Therefore, the goal of the removeCycles :: Spec > Spec
function
defined in TransSys.Transform
is to remove such dependency cycles.
This function relies on the mergeNodes :: [NodeId] > Spec > Spec
function
which signature is selfexplicit. The latter solves name conflicts by using the
Misc.Renaming
monad. Some code complexity has been added so the variable
names remains as clear as possible after merging two nodes.
The function removeCycles
computes the strongly connected components of the
dependency graph and merge each one into a single node. The complexity of this
process is high in the worst case (the square of the total size of the system
times the size of the biggest node) but good in practice as few nodes are to be
merged in most practical cases.
After the cycles have been removed, it is useful to apply another
transformation which makes the translation from TransSys.Spec
to Kind2.AST
easier. This transformation is implemented in the complete
function. In a
nutshell, it transforms a system such that
 If a node depends on another, it imports all its variables.
 The dependency graph is transitive, that is if A depends of B which depends of C then A depends on C.
After this transformation, the translation from TransSys.Spec
to Kind2.AST
is almost only a matter of syntax.
Bonus track
Thanks to the mergeNodes
function, we can get for free the function
inline :: Spec > Spec
inline spec = mergeNodes [nodeId n  n < specNodes spec] spec
which discards all the structure of a modular transition system and turns it
into a nonmodular transition system with only one node. In fact, when
translating a copilot specification to a kind2 file, two styles are available:
the Kind2.toKind2
function takes a Style
argument which can take the value
Inlined
or Modular
. The only difference is that in the first case, a call
to removeCycles
is replaced by a call to inline
.
Limitations of copilottheorem
Now, we will discuss some limitations of the copilottheorem tool. These limitations are organized in two categories: the limitations related to the Copilot language itself and its implementation, and the limitations related to the modelchecking techniques we are using.
Limitations related to Copilot implementation
The reification process used to build the Core.Spec
object looses many
informations about the structure of the original Copilot program. In fact, a
stream is kept in the reified program only if it is recursively defined.
Otherwise, all its occurences will be inlined. Moreover, let's look at the
intCounter
function defined in the example Grey.hs
:
intCounter :: Stream Bool > Stream Word64
intCounter reset = time
where
time = if reset then 0
else [0] ++ if time == 3 then 0 else time + 1
If n counters are created with this function, the same code will be inlined n times and the structure of the original code will be lost.
There are many problems with this:
 It makes some optimizations of the modelchecking based on a static analysis of the program more difficult (for instance structural abstraction  see [2]).
 It makes the inputs given to the SMT solvers larger and repetitive.
We can't rewrite the Copilot reification process in order to avoid these inconvenients as these informations are lost by GHC itself before it occurs. The only solution we can see would be to use Template Haskell to generate automatically some structural annotations, which might not be worth the dirt introduced.
Limitations related to the modelchecking techniques used
##### Limitations of the IC3 algorithm
The IC3 algorithm was shown to be a very powerful tool for hardware certification. However, the problems encountered when verifying softwares are much more complex. For now, very few noninductive properties can be proved by Kind2 when basic integer arithmetic is involved.
The critical point of the IC3 algorithm is the counterexample generalization and the lemma tightening parts of it. When encountering a counterexample to the inductiveness (CTI) for a property, these techniques are used to find a lemma discarding it which is general enough so that all CTIs can be discarded in a finite number of steps.
The lemmas found by the current version fo Kind2 are often too weak. Some suggestions to enhance this are presented in [1]. We hope some progress will be made in this area in a near future.
A workaround to this problem would be to write kind of an interactive mode where the user is invited to provide some additional lemmas when automatic techniques fail. Another solution would be to make the properties being checked quasiinductive by hand. In this case, copilottheorem is still a useful tool (especially for finding bugs) but the verification of a program can be long and requires a high level of technicity.
##### Limitations related to the SMT solvers
The use of SMT solvers introduces two kind of limitations:
 We are limited by the computing power needed by the SMT solvers
 SMT solvers can't handle quantifiers efficiently
Let's consider the first point. SMT solving is costly and its performances are
sometimes unpredictable. For instance, when running the SerialBoyerMoore
example with the light prover, Yices2 does not terminate. However, the z3
SMT solver used by Kind2 solves the problem instantaneously. Note that this
performance gap is not due to the use of the IC3 algorithm because the property
to check is inductive. It could be related to the fact the SMT problem produced
by the light prover uses uninterpreted functions for encoding streams instead
of simple integer variables, which is the case when the copilot program is
translated into a transition system. However, this wouldn't explain why the
light prover still terminates instantaneously on the BoyerMoore
example,
which seems not simpler by far.
The second point keeps you from expressing or proving some properties
universally quantified over a stream or a constant. Sometimes, this is still
possible. For instance, in the Grey
example, as we check a property like
intCounter reset == greyCounter reset
with reset
an external stream
(therefore totally unconstrained), we kind of show a universally quantified
property. This fact could be used to enhance the proof scheme system (see the
Future work section). However, this trick is not always possible. For
instance, in the SerialBoyerMoore
example, the property being checked should
be quantified over all integer constants. Here, we can't just introduce an
arbitrary constant stream because it is the quantified property which is
inductive and not the property specialized for a given constant stream. That's
why we have no other solution than replacing universal quantification by
bounded universal quantification by assuming all the elements of the input
stream are in the finite list allowed
and using the function forAllCst
defined in Copilot.Kind.Lib
:
conj :: [Stream Bool] > Stream Bool
conj = foldl (&&) true
forAllCst ::(Typed a) => [a] > (Stream a > Stream Bool) > Stream Bool
forAllCst l f = conj $ map (f . constant) l
However, this solution isn't completely satisfying because the size of the
property generated is proportionnal to the cardinal of allowed
.
#### Some scalability issues
A standard way to prove large programs is to rely on its logical structure by writing a specification for each of its functions. This very natural approach is hard to follow in our case because of
 The difficulty to deal with universal quantification.
 The lack of true functions in Copilot: the latter offers metaprogramming facilities but no concept of functions like Lustre does with its nodes).
 The inlining policy of the reification process. This point is related to the previous one.
Once again, copilottheorem is still a very useful tool, especially for debugging purposes. However, we don't think it is adapted to write and check a complete specification for large scale programs.
Future work
Missing features in the Kind2 prover
These features are not currently provided due to the lack of important features in the Kind2 SMT solver.
Counterexamples displaying
Counterexamples are not displayed with the Kind2 prover because Kind2 doesn't
support XML output of counterexamples. If the last feature is provided, it
should be easy to implement counterexamples displaying in copilottheorem. For
this, we recommend to keep some informations about observers in
TransSys.Spec
and to add one variable per observer in the Kind2 output file.
Bad handling of nonlinear operators and external functions
Nonlinear Copilot operators and external functions are poorly handled because of the lack of support of uninterpreted functions in the Kind2 native format. A good way to handle these would be to use uninterpreted functions. With this solution, properties like
2 * sin x + 1 <= 3
with x
any stream can't be proven but at least the following can be proved
let y = x in sin x == sin y
Currently, the Kind2 prover fail with the last example, as the results of unknown functions are turned into fresh unconstrained variables.
Simple extensions
The following extensions would be really simple to implement given the current architecture of Kind2.
If inductive proving of a property fails, giving the user a concrete CTI (Counterexample To The Inductiveness, see the [1]).
Use Template Haskell to declare automatically some observers with the same names used in the original program.
Refactoring suggestions
Implement a cleaner way to deal with arbitrary streams and arbitrary constants by extending the
Copilot.Core.Expr type
. See theCopilot.Kind.Lib
module to observe how inelegant the current solution is.Use
Cnub
as an intermediary step in the translation fromCore.Spec
toIL.Spec
andTransSys.Spec
.
More advanced enhancements
Enhance the proof scheme system such that when proving a property depending on an arbitrary stream, it is possible to assume some specialized versions of this property for given values of the arbitrary stream. In other words, implementing a basic way to deal with universal quantification.
It could be useful to extend the Copilot language in a way it is possible to use annotations inside the Copilot code. For instance, we could
 Declare assumptions and invariants next to the associated code instead of gathering all properties in a single place.
 Declare a frequent code pattern which should be factorized in the transition problem (see the section about Copilot limitations)
FAQ
Why does the light prover not deliver counterexamples ?
The problem is the light prover is using uninterpreted functions to represent streams and Yices2 can't give you values for uninterpreted functions when you ask it for a valid assignment. Maybe we could get better performances and easily counterexample display if we rewrite the light prover so that it works with transition systems instead of IL.
### Why does the code related to transition systems look so complex ?
It is true the code of TransSys
is quite complex. In fact, it would be really
straightforward to produce a flattened transition system and then a Kind2 file
with just a single top predicate. In fact, It would be as easy as producing
an IL specification.
To be honest, I'm not sure producing a modular Kind2 output is worth the complexity added. It's especially true at the time I write this in the sense that:
 Each predicate introduced is used only one time (which is true because copilot doesn't handle functions or parametrized streams like Lustre does and everything is inlined during the reification process).
 A similar form of structure could be obtained from a flattened Kind2 native input file with some basic static analysis by producing a dependency graph between variables.
 For now, the Kind2 modelchecker ignores these structure informations.
However, the current code offers some nice transformation tools (node merging,
Renaming
monad...) which could be useful if you intend to write a tool for
simplifying or factorizing transition systems. Moreover, it becomes easier to
write local transformations on transition systems as name conflicts can be
avoided more easily when introducing more variables, as there is one namespace
per node.
References
An insight into SMTbased model checking techniques for formal software verification of synchronous dataflow programs, talk, Jonathan Laurent (see the
doc
folder of this repository)Scaling up the formal verification of Lustre programs with SMTbased techniques, G. Hagen, C. Tinelli
SMTbased Unbounded Model Checking with IC3 and Approximate Quantifier Elimination, C. Sticksel, C. Tinelli
Verifying safety properties of Lustre programs: an SMTbased approach, PhD thesis, G. Hagen
Understanding IC3, Aaron R. Bradley
IC3: Where Monolithic and Incremental Meet, F. Somenzi, A.R. Bradley
Copilot: Monitoring Embedded Systems, L. Pike, N. Wegmann, S. Niller