def getdep(self, target, environ):
if self._case is None or self._case() is None:
raise DependencyError('no test case is associated with this test')
for d in self._case().deps:
if d.check.name == target and d.environ.name == environ:
return d.check
raise DependencyError('could not resolve dependency to (%s, %s)' %
(target, environ))
def resolve_dep(target, from_map, *args):
errmsg = 'could not resolve dependency: %s' % target
try:
ret = from_map[args]
except KeyError:
raise DependencyError(errmsg)
else:
if not ret:
raise DependencyError(errmsg)
return ret
def resolve_dep(src, dst):
errmsg = f'could not resolve dependency: {src!r} -> {dst!r}'
try:
ret = all_cases_map[dst]
except KeyError:
# Try to resolve the dependency in the fallback map
try:
ret = default_cases_map[dst]
except KeyError:
raise DependencyError(errmsg) from None
if not ret:
raise DependencyError(errmsg)
return ret
def resolve_dep(target, from_map, fallback_map, *args):
errmsg = 'could not resolve dependency: %s -> %s' % (target, args)
try:
ret = from_map[args]
except KeyError:
# try to resolve the dependency in the fallback map
try:
ret = fallback_map[args]
except KeyError:
raise DependencyError(errmsg) from None
if not ret:
raise DependencyError(errmsg)
return ret
def validate_deps(graph):
"""Validate dependency graph."""
# Reduce test case graph to a test name only graph; this disallows
# pseudo-dependencies as follows:
#
# (t0, e1) -> (t1, e1)
# (t1, e0) -> (t0, e0)
#
# This reduction step will result in a graph description with duplicate
# entries in the adjacency list; this is not a problem, cos they will be
# filtered out during the DFS traversal below.
test_graph = {}
for case, deps in graph.items():
test_deps = [d.check.name for d in deps]
try:
test_graph[case.check.name] += test_deps
except KeyError:
test_graph[case.check.name] = test_deps
# Check for cyclic dependencies in the test name graph
visited = set()
sources = set(test_graph.keys())
path = []
# Since graph may comprise multiple not connected subgraphs, we search for
# cycles starting from all possible sources
while sources:
unvisited = [(sources.pop(), None)]
while unvisited:
node, parent = unvisited.pop()
while path and path[-1] != parent:
path.pop()
adjacent = reversed(test_graph[node])
path.append(node)
for n in adjacent:
if n in path:
cycle_str = '->'.join(path + [n])
raise DependencyError(
'found cyclic dependency between tests: ' + cycle_str)
if n not in visited:
unvisited.append((n, node))
visited.add(node)
sources -= visited
def validate_deps(graph):
'''Validate dependency graph.'''
# Reduce test case graph to a test name only graph; this disallows
# pseudo-dependencies as follows:
#
# (t0, e1) -> (t1, e1)
# (t1, e0) -> (t0, e0)
#
test_graph = _reduce_deps(graph)
# Check for cyclic dependencies in the test name graph
visited = set()
sources = set(test_graph.keys())
path = []
# Since graph may comprise multiple not connected subgraphs, we search for
# cycles starting from all possible sources
while sources:
unvisited = [(sources.pop(), None)]
while unvisited:
node, parent = unvisited.pop()
while path and path[-1] != parent:
path.pop()
adjacent = test_graph[node]
path.append(node)
for n in adjacent:
if n in path:
cycle_str = '->'.join(path + [n])
raise DependencyError(
'found cyclic dependency between tests: ' + cycle_str)
if n not in visited:
unvisited.append((n, node))
visited.add(node)
sources -= visited