1 files changed, 10 insertions, 5 deletions
diff --git a/src/statistics.cc b/src/statistics.cc
index 844e926..16b6026 100644
--- a/src/statistics.cc
+++ b/src/statistics.cc
@@ -32,7 +32,7 @@ auto StatisticsSum = [](const std::vector<double>& v) {
 
 double StatisticsMean(const std::vector<double>& v) {
   if (v.empty()) return 0.0;
-  return StatisticsSum(v) * (1.0 / v.size());
+  return StatisticsSum(v) * (1.0 / static_cast<double>(v.size()));
 }
 
 double StatisticsMedian(const std::vector<double>& v) {
@@ -71,8 +71,11 @@ double StatisticsStdDev(const std::vector<double>& v) {
   // Sample standard deviation is undefined for n = 1
   if (v.size() == 1) return 0.0;
 
-  const double avg_squares = SumSquares(v) * (1.0 / v.size());
-  return Sqrt(v.size() / (v.size() - 1.0) * (avg_squares - Sqr(mean)));
+  const double avg_squares =
+      SumSquares(v) * (1.0 / static_cast<double>(v.size()));
+  return Sqrt(static_cast<double>(v.size()) /
+              (static_cast<double>(v.size()) - 1.0) *
+              (avg_squares - Sqr(mean)));
 }
 
 double StatisticsCV(const std::vector<double>& v) {
@@ -81,6 +84,8 @@ double StatisticsCV(const std::vector<double>& v) {
   const auto stddev = StatisticsStdDev(v);
   const auto mean = StatisticsMean(v);
 
+  if (std::fpclassify(mean) == FP_ZERO) return 0.0;
+
   return stddev / mean;
 }
 
@@ -92,7 +97,7 @@ std::vector<BenchmarkReporter::Run> ComputeStats(
   auto error_count = std::count_if(reports.begin(), reports.end(),
                                    [](Run const& run) { return run.skipped; });
 
-  if (reports.size() - error_count < 2) {
+  if (reports.size() - static_cast<size_t>(error_count) < 2) {
     // We don't report aggregated data if there was a single run.
     return results;
   }
@@ -174,7 +179,7 @@ std::vector<BenchmarkReporter::Run> ComputeStats(
     // Similarly, if there are N repetitions with 1 iterations each,
     // an aggregate will be computed over N measurements, not 1.
     // Thus it is best to simply use the count of separate reports.
-    data.iterations = reports.size();
+    data.iterations = static_cast<IterationCount>(reports.size());
 
     data.real_accumulated_time = Stat.compute_(real_accumulated_time_stat);
     data.cpu_accumulated_time = Stat.compute_(cpu_accumulated_time_stat);