5.11. SPARK Libraries¶
5.11.1. Big Numbers Library¶
Annotations such as preconditions, postconditions, assertions, loop invariants, are analyzed by GNATprove with the exact same meaning that they have during execution. In particular, evaluating the expressions in an annotation may raise a run-time error, in which case GNATprove will attempt to prove that this error cannot occur, and report a warning otherwise.
In SPARK, scalar types such as integer and floating point types are bounded machine types, so arithmetic computations over them can lead to overflows when the result does not fit in the bounds of the type used to hold it. In some cases, it is convenient to express properties in annotations as they would be expressed in mathematics, where quantities are unbounded, for example:
function Add (X, Y : Integer) return Integer with Pre => X + Y in Integer, Post => Add'Result = X + Y;
The precondition of
Add states that the result of adding its two parameters
should fit in type
Integer. Unfortunately, evaluating this expression will
fail an overflow check, because the result of
X + Y is stored in a temporary
To alleviate this issue, it is possible to use the standard library for big numbers. It contains support for:
Unbounded integers in
Unbounded rational numbers in
Theses libraries define representations for big numbers and basic arithmetic operations over them, as well as conversions from bounded scalar types such as floating point numbers or integer types. Conversion from an integer to a big integer is provided by:
To_Big_Integerin generic package
Ada.Numerics.Big_Numbers.Big_Integersfor all other signed integer types
To_Big_Integerin generic package
Ada.Numerics.Big_Numbers.Big_Integersfor modular integer types
Similarly, the same packages define a function
From_Big_Integer to convert
from a big integer to an integer. A function
Ada.Numerics.Big_Numbers.Big_Reals converts from type
Integer to a big
real and function
To_Big_Real in the same package converts from a big
integer to a big real.
Though these operations do not have postconditions, they are interpreted by GNATprove as the equivalent operations on mathematical integers and real numbers. This allows to benefit from precise support on code using them.
Some functionality of the library are not precisely supported. This includes
in particular conversions to and from strings, conversions of
fixed-point or floating-point types, and
The big number library can be used both in annotations and in actual code, as
it is executable, though of course, using it in production code means incurring
its runtime costs. It can be considered a good trade-off to only use it in
contracts, if they are disabled in production builds. For example, we can
rewrite the precondition of our
Add function with big integers to avoid
function Add (X, Y : Integer) return Integer with Pre => In_Range (To_Big_Integer (X) + To_Big_Integer (Y), Low => To_Big_Integer (Integer'First), High => To_Big_Integer (Integer'Last)), Post => Add'Result = X + Y;
As a more advanced example, it is also possible to introduce a ghost model for
numerical computations on floating point numbers as a mathematical real
number so as to be able to express properties about rounding errors. In the
following snippet, we use the ghost variable
M as a model of the
floating point variable
Y, so we can assert that the result of our
floating point calculations are not too far from the result of the same
computations on real numbers.
declare package Float_Convs is new Float_Conversions (Num => Float); -- Introduce conversions to and from values of type Float subtype Small_Float is Float range -100.0 .. 100.0; function Init return Small_Float with Import; -- Unknown initial value of the computation X : constant Small_Float := Init; Y : Float := X; M : Big_Real := Float_Convs.To_Big_Real (X) with Ghost; -- M is used to mimic the computations done on Y on real numbers begin Y := Y * 100.0; M := M * Float_Convs.To_Big_Real (100.0); Y := Y + 100.0; M := M + Float_Convs.To_Big_Real (100.0); pragma Assert (In_Range (Float_Convs.To_Big_Real (Y) - M, Low => Float_Convs.To_Big_Real (- 0.001), High => Float_Convs.To_Big_Real (0.001))); -- The rounding errors introduced by the floating-point computations -- are not too big. end;
5.11.2. Functional Containers Library¶
To model complex data structures, one often needs simpler, mathematical like
containers. The mathematical containers provided in the SPARK library are
unbounded and may contain indefinite elements. Furthermore, to be usable in
every context, they are neither controlled nor limited. So that these containers
can be used safely, we have made them functional, that is, no primitives are
provided which would allow modifying an existing container. Instead, their API
features functions creating new containers from existing ones. As an example,
functional containers provide no
Insert procedure but rather a function
Add which creates a new container with one more element than its parameter:
function Add (C : Container; E : Element_Type) return Container;
As a consequence, these containers are highly inefficient. They are also memory consuming as the allocated memory is not reclaimed when the container is no longer referenced. Thus, they should in general be used in ghost code and annotations so that they can be removed from the final executable.
There are 3 functional containers, which are part of the GNAT standard library:
Sequences defined in
Functional_Vectors are no more than ordered collections
of elements. In an Ada like manner, the user can choose the range used to index
function Length (S : Sequence) return Count_Type; function Get (S : Sequence; N : Index_Type) return Element_Type;
Functional sets offer standard mathematical set functionalities such as inclusion, union, and intersection. They are neither ordered nor hashed:
function Contains (S : Set; E : Element_Type) return Boolean; function "<=" (Left : Set; Right : Set) return Boolean;
Functional maps offer a dictionary between any two types of elements:
function Has_Key (M : Map; K : Key_Type) return Boolean; function Get (M : Map; K : Key_Type) return Element_Type;
Each functional container type supports iteration as appropriate, so that its elements can easily be quantified over.
These containers can easily be used to model user defined data structures. They were used to this end to annotate and verify a package of allocators (see allocators example in the Examples in the Toolset Distribution). In this example, an allocator featuring a free list implemented in an array is modeled by a record containing a set of allocated resources and a sequence of available resources:
type Status is (Available, Allocated); type Cell is record Stat : Status; Next : Resource; end record; type Allocator is array (Valid_Resource) of Cell; type Model is record Available : Sequence; Allocated : Set; end record;
Functional sets and maps represent elements modulo equivalence. For proof, the range of quantification over their content includes all elements that are equivalent to elements included in the container. On the other hand, for execution, the iteration is only done on elements which have actually been included in the container. This difference may make interaction between test and proof tricky when the equivalence relation is not the equality.
Functional containers do not comply with the ownership policy of SPARK if element or key types are ownership types. Care should be taken to do the required copies when storing these elements/keys inside the container or retrieving them.
5.11.3. Formal Containers Library¶
Containers are generic data structures offering a high-level view of collections of objects, while guaranteeing fast access to their content to retrieve or modify it. The most common containers are lists, vectors, sets and maps, which are defined as generic units in the Ada Standard Library. In critical software where verification objectives severely restrict the use of pointers, containers offer an attractive alternative to pointer-intensive data structures.
The Ada Standard Library defines two kinds of containers:
The controlled containers using dynamic allocation, for example
Ada.Containers.Vectors. They define containers as controlled tagged types, so that memory for the container is automatic reallocated during assignment and automatically freed when the container object’s scope ends.
The bounded containers not using dynamic allocation, for example
Ada.Containers.Bounded_Vectors. They define containers as discriminated tagged types, so that the memory for the container can be reserved at initialization.
Although bounded containers are better suited to critical software development, neither controlled containers nor bounded containers can be used in SPARK, because their API does not lend itself to adding suitable contracts (in particular preconditions) ensuring correct usage in client code.
The formal containers are a variation of the bounded containers with API changes that allow adding suitable contracts, so that GNATprove can prove that client code manipulates containers correctly. There are 7 formal containers, which are part of the GNAT standard library:
Lists, sets and maps can only be used with definite objects (objects for which
the compiler can compute the size in memory, hence not
T'Class). Vectors come in two flavors for definite objects
Formal_Vectors) and indefinite objects (
Lists, sets, maps, and definite vectors are always bounded. Indefinite vectors
can be bounded or unbounded
depending on the value of the formal parameter
Bounded when instantiating
the generic unit. Bounded containers do not use dynamic allocation. Unbounded
vectors use dynamic allocation to expand their internal block of memory.
184.108.40.206. Modified API of Formal Containers¶
The visible specification of formal containers is in SPARK, with suitable contracts on subprograms to ensure correct usage, while their private part and implementation is not in SPARK. Hence, GNATprove can be used to prove correct usage of formal containers in client code, but not to prove that formal containers implement their specification.
Query_Element that iterate over a
container are not defined on formal containers. Specification and analysis of
such procedures that take an access-to-procedure in parameter is beyond the
capabilities of SPARK and GNATprove. See Excluded Ada Features.
Procedures and functions that query the content of a container take the
container in parameter. For example, function
Has_Element that queries if a
container has an element at a given position is declared as follows:
function Has_Element (Container : T; Position : Cursor) return Boolean;
This is different from the API of controlled containers and bounded containers, where it is sufficient to pass a cursor to these subprograms, as the cursor holds a reference to the underlying container:
function Has_Element (Position : Cursor) return Boolean;
Cursors of formal containers do not hold a reference to a specific container, as this would otherwise introduce aliasing between container and cursor variables, which is not supported in SPARK. See Absence of Interferences. As a result, the same cursor can be applied to multiple container objects.
For each container type, the library provides model functions that are used to
annotate subprograms from the API. The different models supply different levels
of abstraction of the container’s functionalities. These model functions are
grouped in Ghost Packages named
The higher level view of a container is usually the mathematical structure of element it represents. We use a sequence for ordered containers such as lists and vectors and a mathematical map for imperative maps. This allows us to specify the effects of a subprogram in a very high level way, not having to consider cursors nor order of elements in a map:
procedure Increment_All (L : in out List) with Post => (for all N in 1 .. Length (L) => Element (Model (L), N) = Element (Model (L)'Old, N) + 1); procedure Increment_All (S : in out Map) with Post => (for all K of Model (S)'Old => Has_Key (Model (S), K)) and (for all K of Model (S) => Has_Key (Model (S)'Old, K) and Get (Model (S), K) = Get (Model (S)'Old, K) + 1);
For sets and maps, there is a lower level model representing the underlying
order used for iteration in the container, as well as the actual values of
elements/keys. It is a sequence of elements/keys. We can use it if we want to
Increment_All on maps that the order and actual values of keys
procedure Increment_All (S : in out Map) with Post => Keys (S) = Keys (S)'Old and (for all K of Model (S) => Get (Model (S), K) = Get (Model (S)'Old, K) + 1);
Finally, cursors are modeled using a functional map linking them to their
position in the container. For example, we can state that the positions of
cursors in a list are not modified by a call to
procedure Increment_All (L : in out List) with Post => Positions (L) = Positions (L)'Old and (for all N in 1 .. Length (L) => Element (Model (L), N) = Element (Model (L)'Old, N) + 1);
Switching between the different levels of model functions allows to express
precise considerations when needed without polluting upper level specifications.
For example, consider a variant of the
List.Find function defined in the
API of formal containers, which returns a cursor holding the value searched if
there is one, and the special cursor
1 2 3 4 5 6 7 8 9
with Element_Lists; use Element_Lists; use Element_Lists.Lists; with Ada.Containers; use Ada.Containers; use Element_Lists.Lists.Formal_Model; function My_Find (L : List; E : Element_Type) return Cursor with SPARK_Mode, Contract_Cases => (Contains (L, E) => Has_Element (L, My_Find'Result) and then Element (L, My_Find'Result) = E, not Contains (L, E) => My_Find'Result = No_Element);
The ghost functions mentioned above are specially useful in Loop Invariants to refer to cursors, and positions of elements in the containers.
For example, here, ghost function
Positions is used in the loop invariant to
query the position of the current cursor in the list, and
Model is used to
specify that the value searched is not contained in the part of the container
already traversed (otherwise the loop would have exited):
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
function My_Find (L : List; E : Element_Type) return Cursor with SPARK_Mode is Cu : Cursor := First (L); begin while Has_Element (L, Cu) loop pragma Loop_Invariant (for all I in 1 .. P.Get (Positions (L), Cu) - 1 => Element (Model (L), I) /= E); if Element (L, Cu) = E then return Cu; end if; Next (L, Cu); end loop; return No_Element; end My_Find;
GNATprove proves that function
My_Find implements its specification:
my_find.adb:8:30: info: loop invariant initialization proved my_find.adb:8:30: info: loop invariant preservation proved my_find.adb:8:49: info: precondition proved my_find.adb:9:33: info: precondition proved my_find.adb:11:10: info: precondition proved my_find.adb:15:07: info: precondition proved my_find.ads:6:03: info: disjoint contract cases proved my_find.ads:6:03: info: complete contract cases proved my_find.ads:7:26: info: contract case proved my_find.ads:8:29: info: precondition proved my_find.ads:9:26: info: contract case proved
Just like functional containers, the formal containers do not comply with the ownership policy of SPARK if element or key types are ownership types. Care should be taken to do the required copies when storing these elements/keys inside the container or retrieving them.
220.127.116.11. Quantification over Formal Containers¶
Quantified Expressions can be used over the content of a formal
container to express that a property holds for all elements of a container
for all) or that a property holds for at least one element of a
For example, we can express that all elements of a formal list of integers are prime as follows:
(for all Cu in My_List => Is_Prime (Element (My_List, Cu)))
On this expression, the GNAT compiler generates code that iterates over
My_List using the functions
Iterable aspect applying to the type of formal lists, so the
quantified expression above is equivalent to:
declare Cu : Cursor_Type := First (My_List); Result : Boolean := True; begin while Result and then Has_Element (My_List, Cu) loop Result := Is_Prime (Element (My_List, Cu)); Cu := Next (My_List, Cu); end loop; end;
Result is the value of the quantified expression. See GNAT
Reference Manual for details on aspect
5.11.4. SPARK Lemma Library¶
As part of the SPARK product, a library of lemmas is available through the
<spark-install>/lib/gnat/spark_lemmas.gpr. Header files of
the lemma library are available through menu item in GNAT Studio. To use this library in a program, you need to
add a corresponding dependency in your project file, for example:
with "spark_lemmas"; project My_Project is ... end My_Project;
You may need to update the environment variable
GPR_PROJECT_PATH for the
lemma library project to be found by GNAT compiler, as described in
Installation of GNATprove.
You also need to set the environment variable
the absolute path of the object directory where you want compilation and
verification artefacts for the lemma library to be created. This should be an
absolute path (not a relative one) otherwise these artefacts will be created
inside you SPARK install.
Finally, if you instantiate in your code a generic from the lemma library, you
also need to pass
-gnateDSPARK_BODY_MODE=Off as a compilation switch for
these generic units.
This library consists in a set of ghost null procedures with contracts (called lemmas). Here is an example of such a lemma:
procedure Lemma_Div_Is_Monotonic (Val1 : Int; Val2 : Int; Denom : Pos) with Global => null, Pre => Val1 <= Val2, Post => Val1 / Denom <= Val2 / Denom;
whose body is simply a null procedure:
procedure Lemma_Div_Is_Monotonic (Val1 : Int; Val2 : Int; Denom : Pos) is null;
This procedure is ghost (as part of a ghost package), which means that the
procedure body and all calls to the procedure are compiled away when producing
the final executable without assertions (when switch -gnata is not set). On
the contrary, when compiling with assertions for testing (when switch -gnata
is set) the precondition of the procedure is executed, possibly detecting
invalid uses of the lemma. However, the main purpose of such a lemma is to
facilitate automatic proof, by providing the prover specific properties
expressed in the postcondition. In the case of
postcondition expresses an inequality between two expressions. You may use this
lemma in your program by calling it on specific expressions, for example:
R1 := X1 / Y; R2 := X2 / Y; Lemma_Div_Is_Monotonic (X1, X2, Y); -- at this program point, the prover knows that R1 <= R2 -- the following assertion is proved automatically: pragma Assert (R1 <= R2);
Note that the lemma may have a precondition, stating in which contexts the
lemma holds, which you will need to prove when calling it. For example, a
precondition check is generated in the code above to show that
X2. Similarly, the types of parameters in the lemma may restrict the contexts
in which the lemma holds. For example, the type
Pos for parameter
Lemma_Div_Is_Monotonic is the type of positive integers. Hence, a range
check may be generated in the code above to show that
Y is positive.
All the lemmas provided in the SPARK lemma library have been proved either
automatically or using Coq interactive prover. The Why3 session file recording
all proofs, as well as the individual Coq proof scripts, are available as part
of the SPARK product under directory
<spark-install>/lib/gnat/proof. For example, the proof of lemma
Lemma_Div_Is_Monotonic is a Coq proof of the mathematical property (in Coq
Currenly, the SPARK lemma library provides the following lemmas:
Lemmas on signed integer arithmetic in file
spark-arithmetic_lemmas.ads, that are instantiated for 32 bits signed integers (
Integer) in file
spark-integer_arithmetic_lemmas.adsand for 64 bits signed integers (
Long_Integer) in file
Lemmas on modular integer arithmetic in file
spark-mod_arithmetic_lemmas.ads, that are instantiated for 32 bits modular integers (
Interfaces.Unsigned_32) in file
spark-mod32_arithmetic_lemmas.adsand for 64 bits modular integers (
Interfaces.Unsigned_64) in file
GNAT-specific lemmas on fixed-point arithmetic in file
spark-fixed_point_arithmetic_lemmas.ads, that need to be instantiated by the user for her specific fixed-point type.
Lemmas on floating point arithmetic in file
spark-floating_point_arithmetic_lemmas.ads, that are instantiated for single-precision floats (
Float) in file
spark-float_arithmetic_lemmas.adsand for double-precision floats (
Long_Float) in file
Lemmas on unconstrained arrays in file
spark-unconstrained_array_lemmas.ads, that need to be instantiated by the user for her specific type of index and element, and specific ordering function between elements.
To apply lemmas to signed or modular integers of different types than the ones used in the instances provided in the library, just convert the expressions passed in arguments, as follows:
R1 := X1 / Y; R2 := X2 / Y; Lemma_Div_Is_Monotonic (Integer(X1), Integer(X2), Integer(Y)); -- at this program point, the prover knows that R1 <= R2 -- the following assertion is proved automatically: pragma Assert (R1 <= R2);
5.11.5. Higher Order Function Library¶
The SPARK product also includes a library of higher order functions for unconstrained arrays. It is available using the same project file as the SPARK Lemma Library.
This library consists in a set of generic entities defining usual operations on
arrays. As an example, here is a generic function for the map higher-level
function on arrays. It applies a given function
F to each element of an
array, returning an array of results in the same order.
generic type Index_Type is range <>; type Element_In is private; type Array_In is array (Index_Type range <>) of Element_In; type Element_Out is private; type Array_Out is array (Index_Type range <>) of Element_Out; with function Init_Prop (A : Element_In) return Boolean; -- Potential additional constraint on values of the array to allow Map with function F (X : Element_In) return Element_Out; -- Function that should be applied to elements of Array_In function Map (A : Array_In) return Array_Out with Pre => (for all I in A'Range => Init_Prop (A (I))), Post => Map'Result'First = A'First and then Map'Result'Last = A'Last and then (for all I in A'Range => Map'Result (I) = F (A (I)));
This function can be instantiated by providing two unconstrained array types
ranging over the same index type and a function
F mapping a component of the
first array type to a component of the second array type. Additionally, a
Init_Prop can be supplied for the components of the first array
to be allowed to apply
F. If no such constraint is needed,
be instantiated with an always
type Nat_Array is array (Positive range <>) of Natural; function Small_Enough (X : Natural) return Boolean is (X < Integer'Last); function Increment_One (X : Integer) return Integer is (X + 1) with Pre => X < Integer'Last; function Increment_All is new SPARK.Higher_Order.Map (Index_Type => Positive, Element_In => Natural, Array_In => Nat_Array, Element_Out => Natural, Array_Out => Nat_Array, Init_Prop => Small_Enough, F => Increment_One);
Increment_All function above will take as an argument an array of
natural numbers small enough to be incremented and will return an array
containing the result of incrementing each number by one:
function Increment_All (A : Nat_Array) return Nat_Array with Pre => (for all I in A'Range => Small_Enough (A (I))), Post => Increment_All'Result'First = A'First and then Increment_All'Result'Last = A'Last and then (for all I in A'Range => Increment_All'Result (I) = Increment_One (A (I)));
Currently, the higher-order function library provides the following functions:
Map functions over unconstrained one-dimensional arrays in file
spark-higher_order.ads. These include both in place and functional map subprograms, with and without an additional position parameter.
Fold functions over unconstrained one-dimensional and two-dimensional arrays in file
spark-higher_order-fold.ads. Both left to right and right to left fold functions are available for one-dimensional arrays. For two-dimensional arrays, fold functions go on a line by line, left to right, top-to-bottom way. For ease of use, these functions have been instantiated for the most common cases.
Sum_2respectively compute the sum of all the elements of a one-dimensional or two-dimensional array, and
Count_2the number of elements with a given
Unlike the SPARK Lemma Library, these generic functions are not verified once and for all as their correction depends on the functions provided at each instance. As a result, each instance should be verified by running the SPARK tools.
5.11.6. SPARK Heap Library¶
To annotate subprograms that allocate and deallocate memory with explicit
Global and Depends contract, a library is available through the project file
<spark-install>/lib/gnat/spark_heap.gpr. To use this library in a
program, you need to add a corresponding dependency in your project file, for
with "spark_heap"; project My_Project is ... end My_Project;
You may need to update
GPR_PROJECT_PATH and set
environment variables, just like for the SPARK Lemma Library.
This library declares an abstract state, that is implicitly referenced by every
occurrence of an allocator and by every call to an instance of the
package SPARK.Heap with SPARK_Mode, Abstract_State => (Dynamic_Memory with External => Async_Writers) is ...
with SPARK.Heap; function New_Integer with Global => SPARK.Heap.Dynamic_Memory, Volatile_Function is Result : T := new Integer'(0); begin return Result; end;
5.11.7. Input-Output Libraries¶
The following text is about
Ada.Text_IO and its child packages,
The effect of functions and procedures of input-output units is partially modelled. This means in particular:
that SPARK functions cannot directly call procedures that do input-output. The solution is either to transform them into procedures, or to hide the effect from GNATprove (if not relevant for analysis) by wrapping the standard input-output procedures in procedures with an explicit
Global => nulland body with
SPARK_Mode => Off.
with Ada.Text_IO; function Foo return Integer is procedure Put_Line (Item : String) with Global => null; procedure Put_Line (Item : String) with SPARK_Mode => Off is begin Ada.Text_IO.Put_Line (Item); end Put_Line; begin Put_Line ("Hello, world!"); return 0; end Foo;
SPARK procedures that call input-output subprograms need to reflect these effects in their Global/Depends contract if they have one.
with Ada.Text_IO; procedure Foo with Global => (Input => Var, In_Out => Ada.Text_IO.File_System) is begin Ada.Text_IO.Put_Line (Var); end Foo; procedure Bar is begin Ada.Text_IO.Put_Line (Var); end Bar;
In the examples above, procedure
Bar have the same
body, but their declarations are different. Global contracts have to
be complete or not present at all. In the case of
Foo, it has an
Input contract on
Var and an
In_Out contract on
File_System, an abstract state from
Ada.Text_IO. Without the
latter contract, a high message would be raised when running
GNATprove. Global contracts will be automatically generated for
Bar by flow analysis if this is user code. Both declarations are
accepted by SPARK.
18.104.22.168. State Abstraction and Global Contracts¶
The abstract state
File_System is used to model the memory on
the system and the file handles (
Col, etc.). This
is explained by the fact that almost every procedure in
that actually modifies attributes of the
File_Type parameter has
in File_Type as a parameter and not
in out. This would be
inconsistent with SPARK rules without the abstract state.
All functions and procedures are annoted with Global, and Pre, Post if
necessary. The Global contracts are most of the time
File_System, even in
Get procedures that update the
current column and/or line. Functions have an
contract. The only functions with
Global => null are the functions
Put in the generic packages that have a similar
behaviour as sprintf and sscanf.
22.214.171.124. Functions and Procedures Removed in SPARK¶
Some functions and procedures are removed from SPARK usage because they are not consistent with SPARK rules:
Standard_Errorare turned off in
SPARK_Modebecause they create aliasing, by returning the corresponding file.
Set_Errorare turned off because they also create aliasing, by assigning a
Current_Inputor the other two.
It is still possible to use
Set_Inputand the 3 others to make the code clearer. This is doable by calling
Set_Inputin a different subprogram whose body has
SPARK_Mode => Off. However, it is necessary to check that the file is open and the mode is correct, because there are no checks made on procedures that do not take a file as a parameter (i.e. implicit, so it will write to/read from the current output/input).
Get_Lineis disabled in SPARK because it is a function with side effects. Even with the
Volatile_Functionattribute, it is not possible to model its action on the files and global variables in SPARK. The function is very convenient because it returns an unconstrained string, but a workaround is possible by constructing the string with a buffer:
with Ada.Text_IO; with Ada.Strings.Unbounded; use Ada.Strings.Unbounded; procedure Echo is Unb_Str : Unbounded_String := Null_Unbounded_String; Buffer : String (1 .. 1024); Last : Natural := 1024; begin while Last = 1024 loop Ada.Text_IO.Get_Line (Buffer, Last); exit when Last > Natural'Last - Length (Unb_Str); Unb_Str := Unb_Str & Buffer (1 .. Last); end loop; declare Str : String := To_String (Unb_Str); begin Ada.Text_IO.Put_Line (Str); end; end Echo;
126.96.36.199. Errors Handling¶
Status_Error (due to a file already open/not open) and
Mode_Error are fully
Layout_Error, which is a special case of a partially
handled error and explained in a few lines below, all other errors are
Use_Erroris related to the external environment.
Name_Errorwould require to check availability on disk beforehand.
End_Erroris raised when a file terminator is read while running the procedure.
Out_File, it is possible to set a
Page_Length. When writing in this file, the procedures will add
Line markers and Page markers each
Line_Length characters or
Page_Length lines respectively.
Layout_Error occurs when
trying to set the current column or line to a value that is greater
Page_Length respectively. This error is
handled when using
However, this error is not handled when no
Page_Length has been specified, e.g, if the lines are unbounded,
it is possible to have a
Col greater than
therefore have a
Layout_Error raised when calling
Not only the handling is partial, but it is also impossible to prove
preconditions when working with two files or more. Since
Line_Length etc. attributes are stored in the
is not posible to prove that the
File_2 has not
been modified when running any procedure that do input-output on
Layout_Error may be raised when calling
Put to display the
value of a real number (floating-point or fixed-point) in a string output
parameter, which is not reflected currently in the precondition of
no simple precondition can describe the required length in such a case.