def testGroupVariablesIfBool(self): du = { (0, 0): [7, 5], (0, 4): [8], (0, 5): [8], (0, 7): [8], (1, 6): [7], (2, -1): [6, 1], (3, -2): [2, 3] } ud = { (0, 5): [0], (0, 7): [0], (0, 8): [4, 5, 7], (1, 7): [6], (2, 1): [-1], (2, 6): [-1], (3, 2): [-2], (3, 3): [-2] } groups = dataflow.group_variables([0, 1, 2, 3], du, ud) expected_groups = { 0: [([0], [5, 7]), ([4, 5, 7], [8])], 1: [([6], [7])], 2: [([-1], [1, 6])], 3: [([-2], [2, 3])] } self.assertItemsEqual(groups, expected_groups) for entry in groups: self.assertItemsEqual(groups[entry], expected_groups[entry])
def testGroupVariablesGCD(self): du = { ('a', -1): [0], ('b', -2): [1], ('c', 0): [2, 5, 6, 7, 8], ('c', 6): [8], ('d', 1): [3, 4, 5, 6, 7], ('ret', 3): [9], ('ret', 8): [9] } ud = { ('a', 0): [-1], ('b', 1): [-2], ('c', 2): [0], ('c', 5): [0], ('c', 6): [0], ('c', 7): [0], ('c', 8): [0, 6], ('d', 3): [1], ('d', 4): [1], ('d', 5): [1], ('d', 6): [1], ('d', 7): [1], ('ret', 9): [3, 8] } expected_groups = { 'a': [([-1], [0])], 'b': [([-2], [1])], 'c': [([0, 6], [8, 2, 5, 6, 7])], 'd': [([1], [3, 4, 5, 6, 7])], 'ret': [([3, 8], [9])] } groups = dataflow.group_variables(['a', 'b', 'c', 'd', 'ret'], du, ud) self.assertItemsEqual(groups, expected_groups)
def testGroupVariablesGCD(self): du = { ('a', -1): [0], ('b', -2): [1], ('c', 0): [2, 5, 6, 7, 8], ('c', 6): [8], ('d', 1): [3, 4, 5, 6, 7], ('ret', 3): [9], ('ret', 8): [9] } ud = { ('a', 0): [-1], ('b', 1): [-2], ('c', 2): [0], ('c', 5): [0], ('c', 6): [0], ('c', 7): [0], ('c', 8): [0, 6], ('d', 3): [1], ('d', 4): [1], ('d', 5): [1], ('d', 6): [1], ('d', 7): [1], ('ret', 9): [3, 8] } expected_groups = { 'a': [([-1], [0])], 'b': [([-2], [1])], 'c': [([0, 6], [8, 2, 5, 6, 7])], 'd': [([1], [3, 4, 5, 6, 7])], 'ret': [([3, 8], [9])] } groups = dataflow.group_variables(['a', 'b', 'c', 'd', 'ret'], du, ud) self.assertEqual(groups, expected_groups)
def testGroupVariablesIfBool(self): du = { (0, 0): [7, 5], (0, 4): [8], (0, 5): [8], (0, 7): [8], (1, 6): [7], (2, -1): [6, 1], (3, -2): [2, 3] } ud = { (0, 5): [0], (0, 7): [0], (0, 8): [4, 5, 7], (1, 7): [6], (2, 1): [-1], (2, 6): [-1], (3, 2): [-2], (3, 3): [-2] } groups = dataflow.group_variables([0, 1, 2, 3], du, ud) expected_groups = { 0: [([0], [5, 7]), ([4, 5, 7], [8])], 1: [([6], [7])], 2: [([-1], [1, 6])], 3: [([-2], [2, 3])] } self.assertItemsEqual(groups, expected_groups) for entry in groups: self.assertItemsEqual(groups[entry], expected_groups[entry])
def process(self, doAST=False): """ Processes the method and decompile the code. There are two modes of operation: 1) Normal Decompilation to Java Code 2) Decompilation into an abstract syntax tree (AST) The Decompilation is done twice. First, a rough decompilation is created, which is then optimized. Second, the optimized version is used to create the final version. :param doAST: generate AST instead of Java Code """ logger.debug('METHOD : %s', self.name) # Native methods... no blocks. if self.start_block is None: logger.debug('Native Method.') if doAST: self.ast = JSONWriter(None, self).get_ast() else: self.writer = Writer(None, self) self.writer.write_method() return # Construct the CFG graph = construct(self.start_block, self.var_to_name, self.exceptions) self.graph = graph if not __debug__: # TODO: use tempfile to create a correct tempfile (cross platform compatible) util.create_png(self.cls_name, self.name, graph, '/tmp/dad/blocks') use_defs, def_uses = build_def_use(graph, self.lparams) split_variables(graph, self.var_to_name, def_uses, use_defs) dead_code_elimination(graph, def_uses, use_defs) register_propagation(graph, def_uses, use_defs) resolve_variables_type(graph, self.var_to_name, def_uses, use_defs) new_instance_propgation(graph, def_uses, use_defs) #for dummy setattr(graph, "group_var", group_variables(self.var_to_name, def_uses, use_defs)) # FIXME var_to_name need to contain the created tmp variables. # This seems to be a workaround, we add them into the list manually for var, i in def_uses: if not isinstance(var, int): self.var_to_name[var] = var.upper() place_declarations(graph, self.var_to_name, def_uses, use_defs) del def_uses, use_defs # After the DCE pass, some nodes may be empty, so we can simplify the # graph to delete these nodes. # We start by restructuring the graph by spliting the conditional nodes # into a pre-header and a header part. split_if_nodes(graph) # We then simplify the graph by merging multiple statement nodes into # a single statement node when possible. This also delete empty nodes. simplify(graph) graph.compute_rpo() if not __debug__: # TODO: use tempfile to create a correct tempfile (cross platform compatible) util.create_png(self.cls_name, self.name, graph, '/tmp/dad/pre-structured') identify_structures(graph, graph.immediate_dominators()) if not __debug__: # TODO: use tempfile to create a correct tempfile (cross platform compatible) util.create_png(self.cls_name, self.name, graph, '/tmp/dad/structured') if doAST: self.ast = JSONWriter(graph, self).get_ast() else: self.writer = Writer(graph, self) self.writer.write_method()