145 lines
4.1 KiB
C
145 lines
4.1 KiB
C
/* Find near-matches for identifiers.
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Copyright (C) 2015-2017 Free Software Foundation, Inc.
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This file is part of GCC.
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GCC is free software; you can redistribute it and/or modify it under
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the terms of the GNU General Public License as published by the Free
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Software Foundation; either version 3, or (at your option) any later
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version.
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GCC is distributed in the hope that it will be useful, but WITHOUT ANY
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WARRANTY; without even the implied warranty of MERCHANTABILITY or
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FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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for more details.
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You should have received a copy of the GNU General Public License
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along with GCC; see the file COPYING3. If not see
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<http://www.gnu.org/licenses/>. */
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#include "config.h"
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#include "system.h"
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#include "coretypes.h"
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#include "tm.h"
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#include "tree.h"
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#include "cpplib.h"
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#include "spellcheck-tree.h"
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#include "selftest.h"
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#include "stringpool.h"
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/* Calculate Levenshtein distance between two identifiers. */
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edit_distance_t
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levenshtein_distance (tree ident_s, tree ident_t)
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{
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gcc_assert (TREE_CODE (ident_s) == IDENTIFIER_NODE);
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gcc_assert (TREE_CODE (ident_t) == IDENTIFIER_NODE);
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return levenshtein_distance (IDENTIFIER_POINTER (ident_s),
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IDENTIFIER_LENGTH (ident_s),
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IDENTIFIER_POINTER (ident_t),
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IDENTIFIER_LENGTH (ident_t));
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}
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/* Given TARGET, an identifier, and CANDIDATES, a vec of identifiers,
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determine which element within CANDIDATES has the lowest edit
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distance to TARGET. If there are multiple elements with the
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same minimal distance, the first in the vector wins.
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If more than half of the letters were misspelled, the suggestion is
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likely to be meaningless, so return NULL_TREE for this case. */
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tree
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find_closest_identifier (tree target, const auto_vec<tree> *candidates)
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{
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gcc_assert (TREE_CODE (target) == IDENTIFIER_NODE);
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best_match<tree, tree> bm (target);
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int i;
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tree identifier;
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FOR_EACH_VEC_ELT (*candidates, i, identifier)
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{
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gcc_assert (TREE_CODE (identifier) == IDENTIFIER_NODE);
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bm.consider (identifier);
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}
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return bm.get_best_meaningful_candidate ();
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}
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/* A callback for cpp_forall_identifiers, for use by best_macro_match's ctor.
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Process HASHNODE and update the best_macro_match instance pointed to be
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USER_DATA. */
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static int
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find_closest_macro_cpp_cb (cpp_reader *, cpp_hashnode *hashnode,
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void *user_data)
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{
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if (hashnode->type != NT_MACRO)
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return 1;
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best_macro_match *bmm = (best_macro_match *)user_data;
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bmm->consider (hashnode);
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/* Keep iterating. */
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return 1;
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}
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/* Constructor for best_macro_match.
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Use find_closest_macro_cpp_cb to find the closest matching macro to
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NAME within distance < best_distance_so_far. */
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best_macro_match::best_macro_match (tree goal,
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edit_distance_t best_distance_so_far,
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cpp_reader *reader)
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: best_match <goal_t, candidate_t> (goal, best_distance_so_far)
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{
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cpp_forall_identifiers (reader, find_closest_macro_cpp_cb, this);
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}
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#if CHECKING_P
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namespace selftest {
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/* Selftests. */
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/* Verify that find_closest_identifier is sane. */
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static void
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test_find_closest_identifier ()
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{
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auto_vec<tree> candidates;
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/* Verify that it can handle an empty vec. */
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ASSERT_EQ (NULL, find_closest_identifier (get_identifier (""), &candidates));
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/* Verify that it works sanely for non-empty vecs. */
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tree apple = get_identifier ("apple");
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tree banana = get_identifier ("banana");
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tree cherry = get_identifier ("cherry");
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candidates.safe_push (apple);
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candidates.safe_push (banana);
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candidates.safe_push (cherry);
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ASSERT_EQ (apple, find_closest_identifier (get_identifier ("app"),
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&candidates));
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ASSERT_EQ (banana, find_closest_identifier (get_identifier ("banyan"),
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&candidates));;
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ASSERT_EQ (cherry, find_closest_identifier (get_identifier ("berry"),
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&candidates));
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ASSERT_EQ (NULL,
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find_closest_identifier (get_identifier ("not like the others"),
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&candidates));
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}
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/* Run all of the selftests within this file. */
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void
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spellcheck_tree_c_tests ()
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{
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test_find_closest_identifier ();
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}
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} // namespace selftest
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#endif /* #if CHECKING_P */
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