<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><br class=""><div><blockquote type="cite" class=""><div class="">On Jun 12, 2017, at 4:45 PM, Pavol Vaskovic <<a href="mailto:pali@pali.sk" class="">pali@pali.sk</a>> wrote:</div><br class="Apple-interchange-newline"><div class=""><div style="font-family: Helvetica; font-size: 12px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px;" class=""><div style="font-size: 12.8px;" class="">I have sketched an algorithm for getting more consistent test results, so far its in Numbers. I have ran the whole test suite for 100 samples and observed the varying distribution of test results. The first result is quite often an outlier, with subsequent results being quicker. Depending on the "weather" on the test machine, you sometimes measure anomalies. So I'm tracking the coefficient of variance from the sample population and purging anomalous results when it exceeds 5%. This results in solid sample population where standard deviation is a meaningful value, that can be use in judging the significance of change between master and branch.</div></div></div></blockquote><br class=""></div><div>That’s a reasonable approach for running 100 samples. I’m not sure how it fits with the goal of minimizing turnaround time. Typically you don’t need more than 3 samples (keeping in mind were usually averaging over thousands of iterations per sample).</div><div><br class=""></div><div>-Andy</div></body></html>