Avoid precision loss in TrendlineEstimator from int64_t -> double conversion

webrtc/modules/congestion_controller/trendline_estimator_unittest.cc

 /*
  *  Copyright (c) 2016 The WebRTC project authors. All Rights Reserved.
  *
  *  Use of this source code is governed by a BSD-style license
  *  that can be found in the LICENSE file in the root of the source
  *  tree. An additional intellectual property rights grant can be found
  *  in the file PATENTS.  All contributing project authors may
  *  be found in the AUTHORS file in the root of the source tree.
  */
 
 #include "webrtc/test/gtest.h"
 #include "webrtc/base/random.h"
 #include "webrtc/modules/congestion_controller/trendline_estimator.h"
 
 namespace webrtc {
 
 namespace {
-constexpr size_t kWindowSize = 15;
+constexpr size_t kWindowSize = 20;
 constexpr double kSmoothing = 0.0;
 constexpr double kGain = 1;
 constexpr int64_t kAvgTimeBetweenPackets = 10;
+constexpr size_t kPacketCount = 2 * kWindowSize + 1;
 }  // namespace
 
-TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
+void TestEstimator(double slope, double jitter_stddev, double tolerance) {
   TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
-  Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
-  for (size_t i = 1; i < 2 * kWindowSize; i++) {
-    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
-    double recv_delta = 2 * send_delta;
-    now_ms += recv_delta;
-    estimator.Update(recv_delta, send_delta, now_ms);
+  Random random(0x1234567);
+  int64_t send_times[kPacketCount];
+  int64_t recv_times[kPacketCount];
+  int64_t send_start_time = random.Rand(1000000);
+  int64_t recv_start_time = random.Rand(1000000);
+  for (size_t i = 0; i < kPacketCount; i++) {

[brandtr, 2016/12/15 12:06:08]

I think we often write ++i in for loops.

[terelius, 2016/12/15 13:11:08]

Done. However, the style guide explicitly allows either and they are used almost
equally often in the code base. Interestingly, the choice depends on the name of
the variable; k++ is preferred to ++k with over 80% of the cases, but ++i is
preferred to i++ with 70% of all cases.

+    send_times[i] = send_start_time + i * kAvgTimeBetweenPackets;
+    double latency = i * kAvgTimeBetweenPackets / (1 - slope);
+    double jitter = random.Gaussian(0, jitter_stddev);
+    recv_times[i] = recv_start_time + latency + jitter;
+  }
+  for (size_t i = 1; i < kPacketCount; i++) {

[brandtr, 2016/12/15 12:06:08]

Here too.

[terelius, 2016/12/15 13:11:08]

Done.

+    double recv_delta = recv_times[i] - recv_times[i - 1];
+    double send_delta = send_times[i] - send_times[i - 1];
+    estimator.Update(recv_delta, send_delta, recv_times[i]);
     if (i < kWindowSize)
       EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
     else
-      EXPECT_NEAR(estimator.trendline_slope(), 0.5, 0.001);
+      EXPECT_NEAR(estimator.trendline_slope(), slope, tolerance);
   }
 }
 
+TEST(TrendlineEstimator, PerfectLineSlopeOneHalf) {
+  TestEstimator(0.5, 0, 0.001);
+}
+
 TEST(TrendlineEstimator, PerfectLineSlopeMinusOne) {
-  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
-  Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
-  for (size_t i = 1; i < 2 * kWindowSize; i++) {
-    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
-    double recv_delta = 0.5 * send_delta;
-    now_ms += recv_delta;
-    estimator.Update(recv_delta, send_delta, now_ms);
-    if (i < kWindowSize)
-      EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
-    else
-      EXPECT_NEAR(estimator.trendline_slope(), -1, 0.001);
-  }
+  TestEstimator(-1, 0, 0.001);
 }
 
 TEST(TrendlineEstimator, PerfectLineSlopeZero) {
-  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
-  Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
-  for (size_t i = 1; i < 2 * kWindowSize; i++) {
-    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
-    double recv_delta = send_delta;
-    now_ms += recv_delta;
-    estimator.Update(recv_delta, send_delta, now_ms);
-    EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
-  }
+  TestEstimator(0, 0, 0.001);
 }
 
 TEST(TrendlineEstimator, JitteryLineSlopeOneHalf) {
-  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
-  Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
-  for (size_t i = 1; i < 2 * kWindowSize; i++) {
-    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
-    double recv_delta = 2 * send_delta + rand.Gaussian(0, send_delta / 3);
-    now_ms += recv_delta;
-    estimator.Update(recv_delta, send_delta, now_ms);
-    if (i < kWindowSize)
-      EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
-    else
-      EXPECT_NEAR(estimator.trendline_slope(), 0.5, 0.1);
-  }
+  TestEstimator(0.5, kAvgTimeBetweenPackets / 3.0, 0.01);
 }
 
 TEST(TrendlineEstimator, JitteryLineSlopeMinusOne) {
-  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
-  Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
-  for (size_t i = 1; i < 2 * kWindowSize; i++) {
-    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
-    double recv_delta = 0.5 * send_delta + rand.Gaussian(0, send_delta / 25);
-    now_ms += recv_delta;
-    estimator.Update(recv_delta, send_delta, now_ms);
-    if (i < kWindowSize)
-      EXPECT_NEAR(estimator.trendline_slope(), 0, 0.001);
-    else
-      EXPECT_NEAR(estimator.trendline_slope(), -1, 0.1);
-  }
+  TestEstimator(-1, kAvgTimeBetweenPackets / 3.0, 0.075);
 }
 
 TEST(TrendlineEstimator, JitteryLineSlopeZero) {
-  TrendlineEstimator estimator(kWindowSize, kSmoothing, kGain);
-  Random rand(0x1234567);
-  double now_ms = rand.Rand<double>() * 10000;
-  for (size_t i = 1; i < 2 * kWindowSize; i++) {
-    double send_delta = rand.Rand<double>() * 2 * kAvgTimeBetweenPackets;
-    double recv_delta = send_delta + rand.Gaussian(0, send_delta / 8);
-    now_ms += recv_delta;
-    estimator.Update(recv_delta, send_delta, now_ms);
-    EXPECT_NEAR(estimator.trendline_slope(), 0, 0.1);
-  }
+  TestEstimator(0, kAvgTimeBetweenPackets / 3.0, 0.02);
 }
 
 }  // namespace webrtc