The B Rules domain-specific language mainly provides operations for data validation. Rules allow checking for expected properties, while computations can be used to define and compute variables based on the successful execution of certain rules. Furthermore you can use functions to compute values multiple times depending on different inputs.
Rules machines are stored in .rmch-files. The general setup for the machine header is:
RULES_MACHINE machine_name REFERENCES list of rules machines
The latter allows inclusion of other rules machines. Below, you can use SETS, DEFINITIONS, PROPERTIES or CONSTANTS as in a normal B machine. Note that VARIABLES are not allowed as they are set by rule based computations.
Rules can be defined in the OPERATIONS-section of a rules machine. Depending on whether the expectations are met, a rule returns SUCCESS or FAIL. If a rule fails, additionally provided string messages are returned as counterexamples.
In the B Rules-DSL a rule has the following structure:
RULE rule_name // will be the name of the operation and variable storing the result
DEPENDS_ON_RULE list of rules
DEPENDS_ON_COMPUTATION list of computations
ACTIVATION predicate
ERROR_TYPES positive number of error types
BODY
arbitrarily many rule bodys (see below)
END
If a rule depends on other rules, it can only be executed if the specified rules have been successfully checked, i.e. their corresponding variables rule_name have the value SUCCESS. In addition, rules can depend on computations. In this case, a rule is enabled when the specified calculation has been executed. You can also specify an individual ACTIVATION predicate. You can omit all activation predicates and dependencies if they are not needed.
If you want to use different error types, e.g. if you use multiple bodys in one rule and want to distinguish between them, you must declare the number of error types in the rule header. Error types are also optional.
Within the body you can use as many conditions for the outer rule as you like. There are two constructs for specifiying the expectation of the rule.
The following is formulated in a positive way, i.e. the execution of the rule leads to SUCCESS if the conditions in the EXPECT-part are fulfilled.
RULE_FORALL
list of identifiers
WHERE
conditions on identifiers
EXPECT
conditions that must be fulfilled for this rule
ERROR_TYPE
number encoding error type, must be in range of error types
COUNTEREXAMPLE
STRING_FORMAT("errorMessage ~w", identifier from list)
END
Alternatively, you can also formulate the rule negatively, i.e. the execution of the rule leads to FAIL if the conditions in the WHEN-part are fulfilled.
RULE_FAIL
list of identifiers
WHEN
conditions on identifiers for a failing rule
ERROR_TYPE
number encoding error type, must be in range of error types
COUNTEREXAMPLE
STRING_FORMAT("errorMessage ~w", identifier from list)
END
Counterexamples are of the type INTEGER <-> STRING. The integer contains the error type, while the string contains the message of the counterexample.
Also valid for the rules header, but not currently used, are:
RULE_ID id CLASSIFICATION identifier
Computations can be used to create variables. As for rules, their activation can depend on further rules, computations or any other predicate specified as an activation condition. Furthermore, a DUMMY_VALUE can be set, which initialises the variable with the specified value instead of the empty set before execution of the computation. The general syntax for computations:
COMPUTATION computation_name
DEPENDS_ON_RULE list of rules
DEPENDS_ON_COMPUTATION list of computations
ACTIVATION predicate
REPLACES identifier of exactly one computation
BODY
DEFINE variable_name
TYPE type of variable
DUMMY_VALUE value of variable before execution (initialisation)
VALUE value of variable after execution
END
END
You can omit all activation predicates and dependencies and also the dummy value if they are not needed. After the execution of a computation, the value of the corresponding variable computation_name is changed from NOT_EXECUTED to EXECUTED.
Since the result of each computation is written into a variable, it is also possible to formulate invariants for these variables.
Functions can be called from any rules machine that references the machine containing the function. Depending on input parameters that must fulfill specified preconditions, the functions returns output value(s) that must fulfill optional postconditions. In the body you can use any B statements to calculate the output value.
FUNCTION output <-- function_name(list of input parameters)
PRECONDITION
predicate
POSTCONDITION
predicate
BODY
some B statements
output := ...
END
If you want to validate data, for example from a CSV or XML file, you can use the rule language. Set up a rules machine as described above and add your data using the appropriate definition. For an XML file, this could look as follows:
RULES_MACHINE XML_import
DEFINITIONS
"LibraryXML.def"
CONSTANTS
xml_data
PROPERTIES
xml_data = READ_XML("xml_file.xml", "auto")
END
Now some properties can be validated. For example:
RULE is_supported_version_of_type_xyz
ERROR_TYPES 2
BODY
RULE_FAIL e
WHEN
1 : dom(xml_data) & e = data(1)'element & e /= "xyz"
ERROR_TYPE 1 // optional: 1 is standard type
COUNTEREXAMPLE
STRING_FORMAT("Error: could not find element 'xyz', was '"^e^"'")
END;
RULE_FAIL v
WHEN
v = xml_data(1)'attributes("version") & v /: supported_versions
ERROR_TYPE 2
COUNTEREXAMPLE
"xyz of version "^v^" is currently not supported"
END
END;
Currently, it is not possible to include rules machines directly into any other machines. Instead, use the rules machine at the top of the hierarchy (of the rules project) and save the internal generated machine as .mch. After changing the machine name accordingly, the rules can be included and used via this machine.