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diff --git a/comm/mailnews/extensions/bayesian-spam-filter/test/unit/test_traitAliases.js b/comm/mailnews/extensions/bayesian-spam-filter/test/unit/test_traitAliases.js
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+/* This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
+
+// Tests bayes trait analysis with aliases. Adapted from test_traits.js
+
+/*
+ * These tests rely on data stored in a file, with the same format as traits.dat,
+ * that was trained in the following manner. There are two training messages,
+ * included here as files aliases1.eml and aliases2.eml  Aliases.dat was trained on
+ * each of these messages, for different trait indices, as follows, with
+ * columns showing the training count for each trait index:
+ *
+ *     file   count(1001)  count(1005) count(1007) count(1009)
+ *
+ *   aliases1.eml      1            0           2           0
+ *   aliases2.eml      0            1           0           1
+ *
+ * There is also a third email file, aliases3.eml, which combines tokens
+ * from aliases1.eml and aliases2.eml
+ *
+ * The goal here is to demonstrate that traits 1001 and 1007, and traits
+ * 1005 and 1009, can be combined using aliases. We classify messages with
+ * trait 1001 as the PRO trait, and 1005 as the ANTI trait.
+ *
+ * With these characteristics, I've run a trait analysis without aliases, and
+ * determined that the following is the correct percentage results from the
+ * analysis for each message. "Train11" means that the training was 1 pro count
+ * from aliases1.eml, and 1 anti count from alias2.eml. "Train32" is 3 pro counts,
+ * and 2 anti counts.
+ *
+ *                 percentage
+ *    file         Train11       Train32
+ *
+ * alias1.eml        92             98
+ * alias2.eml         8              3
+ * alias3.eml        50             53
+ */
+
+var { MailServices } = ChromeUtils.import(
+  "resource:///modules/MailServices.jsm"
+);
+
+var traitService = Cc["@mozilla.org/msg-trait-service;1"].getService(
+  Ci.nsIMsgTraitService
+);
+var kProTrait = 1001;
+var kAntiTrait = 1005;
+var kProAlias = 1007;
+var kAntiAlias = 1009;
+
+var gTest; // currently active test
+
+// The tests array defines the tests to attempt. Format of
+// an element "test" of this array:
+//
+//   test.fileName: file containing message to test
+//   test.proAliases: array of aliases for the pro trait
+//   test.antiAliases: array of aliases for the anti trait
+//   test.percent: expected results from the classifier
+
+var tests = [
+  {
+    fileName: "aliases1.eml",
+    proAliases: [],
+    antiAliases: [],
+    percent: 92,
+  },
+  {
+    fileName: "aliases2.eml",
+    proAliases: [],
+    antiAliases: [],
+    percent: 8,
+  },
+  {
+    fileName: "aliases3.eml",
+    proAliases: [],
+    antiAliases: [],
+    percent: 50,
+  },
+  {
+    fileName: "aliases1.eml",
+    proAliases: [kProAlias],
+    antiAliases: [kAntiAlias],
+    percent: 98,
+  },
+  {
+    fileName: "aliases2.eml",
+    proAliases: [kProAlias],
+    antiAliases: [kAntiAlias],
+    percent: 3,
+  },
+  {
+    fileName: "aliases3.eml",
+    proAliases: [kProAlias],
+    antiAliases: [kAntiAlias],
+    percent: 53,
+  },
+];
+
+// main test
+function run_test() {
+  localAccountUtils.loadLocalMailAccount();
+
+  // load in the aliases trait testing file
+  MailServices.junk
+    .QueryInterface(Ci.nsIMsgCorpus)
+    .updateData(do_get_file("resources/aliases.dat"), true);
+  do_test_pending();
+
+  startCommand();
+}
+
+var listener = {
+  // nsIMsgTraitClassificationListener implementation
+  onMessageTraitsClassified(aMsgURI, aTraits, aPercents) {
+    // print("Message URI is " + aMsgURI);
+    if (!aMsgURI) {
+      // Ignore end-of-batch signal.
+      return;
+    }
+
+    Assert.equal(aPercents[0], gTest.percent);
+    // All done, start the next test
+    startCommand();
+  },
+};
+
+// start the next test command
+function startCommand() {
+  if (!tests.length) {
+    // Do we have more commands?
+    // no, all done
+    do_test_finished();
+    return;
+  }
+
+  gTest = tests.shift();
+
+  // classify message
+  var antiArray = [kAntiTrait];
+  var proArray = [kProTrait];
+
+  // remove any existing aliases
+  let proAliases = traitService.getAliases(kProTrait);
+  let antiAliases = traitService.getAliases(kAntiTrait);
+  let proAlias;
+  let antiAlias;
+  while ((proAlias = proAliases.pop())) {
+    traitService.removeAlias(kProTrait, proAlias);
+  }
+  while ((antiAlias = antiAliases.pop())) {
+    traitService.removeAlias(kAntiTrait, antiAlias);
+  }
+
+  // add new aliases
+  while ((proAlias = gTest.proAliases.pop())) {
+    traitService.addAlias(kProTrait, proAlias);
+  }
+  while ((antiAlias = gTest.antiAliases.pop())) {
+    traitService.addAlias(kAntiTrait, antiAlias);
+  }
+
+  MailServices.junk.classifyTraitsInMessage(
+    getSpec(gTest.fileName), // in string aMsgURI
+    proArray, // in array aProTraits,
+    antiArray, // in array aAntiTraits
+    listener
+  ); // in nsIMsgTraitClassificationListener aTraitListener
+  // null,      // [optional] in nsIMsgWindow aMsgWindow
+  // null,      // [optional] in nsIJunkMailClassificationListener aJunkListener
+}