Before and after method were not actually called in the test suite process.
This PR enables the compilation and run of these methods.
The new behavior is: if the `before_module` test case fails, all the test cases and the `after_module` are skipped and marked as failed.
Pull-Request: #2359
Reviewed-by: Jean-Christophe Beaupré <jcbrinfo.public@gmail.com>
Reviewed-by: Jean Privat <jean@pryen.org>
in "C" `{
-#ifdef __MACH__
+#if defined(__MACH__) && !defined(CLOCK_REALTIME)
/* OS X does not have clock_gettime, mascarade it and use clock_get_time
* cf http://stackoverflow.com/questions/11680461/monotonic-clock-on-osx
*/
# Smallest time frame reported by clock
fun resolution: Timespec `{
struct timespec* tv = malloc( sizeof(struct timespec) );
-#ifdef __MACH__
+#if defined(__MACH__) && !defined(CLOCK_REALTIME)
clock_serv_t cclock;
int nsecs;
mach_msg_type_number_t count;
# (and `build_module_importation` that calls it).
#
# TODO (when the loader will be rewritten): use a better representation and move up rules in the model.
- private var conditional_importations = new Array[SequenceRead[MModule]]
+ var conditional_importations = new Array[SequenceRead[MModule]]
# Extends the current importations according to imported rules about conditional importation
fun apply_conditional_importations(mmodule: MModule)
for i in [1..ci.length[ do
var m = ci[i]
# Is imported?
- if not mmodule.in_importation.greaters.has(m) then continue label
+ if mmodule == m or not mmodule.in_importation.greaters.has(m) then continue label
end
# Still here? It means that all conditions modules are loaded and imported
var location = mainmodule.location
var model = mainmodule.model
+ # Create a fictive module if needed
if mainmodule == mmodules.first then
mainmodule = new MModule(model, null, mainmodule.name + "-d", location)
mainmodule.set_imported_mmodules(mmodules)
mainmodule.is_fictive = true
+ mainmodule.first_real_mmodule = mmodules.first
end
var recv = mainmodule.sys_type
# Is `self` a unit test module used by `nitunit`?
var is_test_suite: Bool = false is writable
- # Get the first non `is_fictive` module greater than self
- fun first_real_mmodule: MModule
- do
- var mmodule = self
- while mmodule.is_fictive do
- mmodule = mmodule.in_importation.direct_greaters.first
- end
- return mmodule
- end
+ # Get the non-`is_fictive` module on which `self` is based on.
+ #
+ # On non-fictive module, this returns `self`.
+ # On fictive modules, this is used to refer the module which `self` is based on.
+ #
+ # This attribute should be set when a fictive module is created. See `is_fictive`.
+ var first_real_mmodule: MModule = self is writable
redef fun parent_concern do return mgroup
end
do
assert not mmodules.is_empty
var mainmodule
- if mmodules.length == 1 then
+ # We need a main module, so we build it by importing all modules
+ mainmodule = new MModule(modelbuilder.model, null, mmodules.first.name + "-m", new Location(mmodules.first.location.file, 0, 0, 0, 0))
+ mainmodule.is_fictive = true
+ mainmodule.first_real_mmodule = mmodules.first.first_real_mmodule
+ mainmodule.set_imported_mmodules(mmodules)
+ modelbuilder.apply_conditional_importations(mainmodule)
+ if mainmodule.in_importation.direct_greaters.length == 1 and mainmodule.in_importation.direct_greaters.first == mmodules.first then
+ # Drop the fictive module if not needed
mainmodule = mmodules.first
else
- # We need a main module, so we build it by importing all modules
- mainmodule = new MModule(modelbuilder.model, null, mmodules.first.name + "-m", new Location(mmodules.first.location.file, 0, 0, 0, 0))
- mainmodule.is_fictive = true
- mainmodule.set_imported_mmodules(mmodules)
- modelbuilder.apply_conditional_importations(mainmodule)
+ # Or else run phases on it
modelbuilder.run_phases
end
return mainmodule
end
end
+ # Load module `filename` and add it as a conditional importation of `mmodule`.
+ #
+ # This means that current (and future) submodules of `module` will also import `filename`.
+ fun inject_module_subimportation(mmodule: MModule, filename: String)
+ do
+ var am = load_module(filename)
+ if am == null then return # forward error
+ var mm = am.mmodule
+ if mm == null then return # forward error
+ # Add the new module before the existing submodules in the hierarchy
+ for subm in mmodule.in_importation.direct_smallers do
+ subm.set_imported_mmodules([mm])
+ end
+ # Register the new module as a conditional_importations for future submodules
+ conditional_importations.add([mm, mmodule])
+ # Register the new amodule to be processed by `run_phases`
+ toolcontext.todo_nmodules.unshift am
+ end
end
# Set of already analyzed modules.
private var phased_modules = new HashSet[AModule]
+ # List of module to process according to `run_phases`
+ #
+ # This allow some new modules to be found and added while analysing the code.
+ var todo_nmodules: Sequence[AModule]
+
# Run all registered phases on a set of modules
fun run_phases(nmodules: Collection[AModule])
do
self.info(" registered phases: {phase}", 2)
end
- for nmodule in nmodules do
+ var todo_nmodules = nmodules.to_a
+ self.todo_nmodules = todo_nmodules
+
+ while not todo_nmodules.is_empty do
+ var nmodule = todo_nmodules.shift
if phased_modules.has(nmodule) then continue
phased_modules.add nmodule
total: 0
--- Poset metrics ---
## Module importation hierarchy
-Number of nodes: 1
-Number of edges: 1 (1.00 per node)
-Number of direct edges: 0 (0.00 per node)
+Number of nodes: 2
+Number of edges: 3 (1.50 per node)
+Number of direct edges: 1 (0.50 per node)
Distribution of greaters
- population: 1
+ population: 2
minimum value: 1
- maximum value: 1
- total value: 1
- average value: 1.00
+ maximum value: 2
+ total value: 3
+ average value: 1.50
distribution:
- <=1: sub-population=1 (100.00%); cumulated value=1 (100.00%)
+ <=1: sub-population=1 (50.00%); cumulated value=1 (33.33%)
+ <=2: sub-population=1 (50.00%); cumulated value=2 (66.66%)
Distribution of direct greaters
- population: 1
+ population: 2
minimum value: 0
- maximum value: 0
- total value: 0
- average value: 0.00
+ maximum value: 1
+ total value: 1
+ average value: 0.50
distribution:
- <=0: sub-population=1 (100.00%); cumulated value=0 (na%)
+ <=0: sub-population=1 (50.00%); cumulated value=0 (0.00%)
+ <=1: sub-population=1 (50.00%); cumulated value=1 (100.00%)
Distribution of smallers
- population: 1
+ population: 2
minimum value: 1
- maximum value: 1
- total value: 1
- average value: 1.00
+ maximum value: 2
+ total value: 3
+ average value: 1.50
distribution:
- <=1: sub-population=1 (100.00%); cumulated value=1 (100.00%)
+ <=1: sub-population=1 (50.00%); cumulated value=1 (33.33%)
+ <=2: sub-population=1 (50.00%); cumulated value=2 (66.66%)
Distribution of direct smallers
- population: 1
+ population: 2
minimum value: 0
- maximum value: 0
- total value: 0
- average value: 0.00
+ maximum value: 1
+ total value: 1
+ average value: 0.50
distribution:
- <=0: sub-population=1 (100.00%); cumulated value=0 (na%)
+ <=0: sub-population=1 (50.00%); cumulated value=0 (0.00%)
+ <=1: sub-population=1 (50.00%); cumulated value=1 (100.00%)
## Classdef hierarchy
Number of nodes: 7
Number of edges: 13 (1.85 per node)
<=0: sub-population=6 (85.71%); cumulated value=0 (0.00%)
<=8: sub-population=1 (14.28%); cumulated value=6 (100.00%)
--- Metrics of refinement usage ---
-Number of modules: 1
+Number of modules: 2
Number of classes: 7
Number of interface kind: 1 (14.28%)
std: 0.0
sum: 0
mnoc: number of child modules
- avg: 0.0
- max: base_simple3 (0)
- min: base_simple3 (0)
+ avg: 1.0
+ max: base_simple3 (1)
+ min: base_simple3 (1)
std: 0.0
- sum: 0
+ sum: 1
mnod: number of descendant modules
- avg: 0.0
- max: base_simple3 (0)
- min: base_simple3 (0)
+ avg: 1.0
+ max: base_simple3 (1)
+ min: base_simple3 (1)
std: 0.0
- sum: 0
+ sum: 1
mdit: depth in module tree
avg: 0.0
max: base_simple3 (0)
std: 0.0
sum: 0
mnoc: number of child modules
- avg: 0.0
- max: base_simple3 (0)
- min: base_simple3 (0)
+ avg: 1.0
+ max: base_simple3 (1)
+ min: base_simple3 (1)
std: 0.0
- sum: 0
+ sum: 1
mnod: number of descendant modules
- avg: 0.0
- max: base_simple3 (0)
- min: base_simple3 (0)
+ avg: 1.0
+ max: base_simple3 (1)
+ min: base_simple3 (1)
std: 0.0
- sum: 0
+ sum: 1
mdit: depth in module tree
avg: 0.0
max: base_simple3 (0)
It works
I have 1 packages
-I have 1 modules
+I have 2 modules
I have 7 classes
For 7 definitions of classes
I have 15 methods
nitlanguage / nit.git / commitdiff