Avoid precision loss in TrendlineEstimator from int64_t -> double conversion

webrtc/modules/congestion_controller/trendline_estimator_unittest.cc

Index: webrtc/modules/congestion_controller/trendline_estimator_unittest.cc
diff --git a/webrtc/modules/congestion_controller/trendline_estimator_unittest.cc b/webrtc/modules/congestion_controller/trendline_estimator_unittest.cc
index 51778e6cf37f2b099b0c518b070b363ba4473027..cf2d021af00c0c63f9ee4dc47987d34de16a0b2d 100644
--- a/webrtc/modules/congestion_controller/trendline_estimator_unittest.cc
+++ b/webrtc/modules/congestion_controller/trendline_estimator_unittest.cc
@@ -15,100 +15,59 @@
 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;
-}  // namespace
+constexpr size_t kPacketCount = 2 * kWindowSize + 1;
 
-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) {
+    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) {
+    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);
   }
 }
+}  // namespace
+
+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