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/****************************************************************************
**
** Copyright (C) 2022 The Qt Company Ltd.
** Contact: https://www.qt.io/licensing/
**
** This file is part of the tools applications of the Qt Toolkit.
**
** $QT_BEGIN_LICENSE:GPL-EXCEPT$
** Commercial License Usage
** Licensees holding valid commercial Qt licenses may use this file in
** accordance with the commercial license agreement provided with the
** Software or, alternatively, in accordance with the terms contained in
** a written agreement between you and The Qt Company. For licensing terms
** and conditions see https://www.qt.io/terms-conditions. For further
** information use the contact form at https://www.qt.io/contact-us.
**
** GNU General Public License Usage
** Alternatively, this file may be used under the terms of the GNU
** General Public License version 3 as published by the Free Software
** Foundation with exceptions as appearing in the file LICENSE.GPL3-EXCEPT
** included in the packaging of this file. Please review the following
** information to ensure the GNU General Public License requirements will
** be met: https://www.gnu.org/licenses/gpl-3.0.html.
**
** $QT_END_LICENSE$
**
****************************************************************************/
#include "qqmljsutils_p.h"
#include <algorithm>
std::optional<FixSuggestion> QQmlJSUtils::didYouMean(const QString &userInput,
QStringList candidates,
QQmlJS::SourceLocation location)
{
QString shortestDistanceWord;
int shortestDistance = userInput.length();
// Most of the time the candidates are keys() from QHash, which means that
// running this function in the seemingly same setup might yield different
// best cadidate (e.g. imagine a typo 'thing' with candidates 'thingA' vs
// 'thingB'). This is especially flaky in e.g. test environment where the
// results may differ (even when the global hash seed is fixed!) when
// running one test vs the whole test suite (recall platform-dependent
// QSKIPs). There could be user-visible side effects as well, so just sort
// the candidates to guarantee consistent results
std::sort(candidates.begin(), candidates.end());
for (const QString &candidate : candidates) {
/*
* Calculate the distance between the userInput and candidate using Damerau–Levenshtein
* Roughly based on
* https://en.wikipedia.org/wiki/Levenshtein_distance#Iterative_with_two_matrix_rows.
*/
QList<int> v0(candidate.length() + 1);
QList<int> v1(candidate.length() + 1);
std::iota(v0.begin(), v0.end(), 0);
for (qsizetype i = 0; i < userInput.length(); i++) {
v1[0] = i + 1;
for (qsizetype j = 0; j < candidate.length(); j++) {
int deletionCost = v0[j + 1] + 1;
int insertionCost = v1[j] + 1;
int substitutionCost = userInput[i] == candidate[j] ? v0[j] : v0[j] + 1;
v1[j + 1] = std::min({ deletionCost, insertionCost, substitutionCost });
}
std::swap(v0, v1);
}
int distance = v0[candidate.length()];
if (distance < shortestDistance) {
shortestDistanceWord = candidate;
shortestDistance = distance;
}
}
if (shortestDistance
< std::min(std::max(userInput.length() / 2, qsizetype(3)), userInput.length())) {
return FixSuggestion { { FixSuggestion::Fix {
u"Did you mean \"%1\"?"_qs.arg(shortestDistanceWord), location,
shortestDistanceWord } } };
} else {
return {};
}
}
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