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Change point detection has recently gained popularity as a method ofdetecting performance changes in software due to its ability to cope with noisydata. In this paper we present Hunter, an open source tool that automaticallydetects performance regressions and improvements in time-series data. Hunteruses a modified E-divisive means algorithm to identify statisticallysignificant changes in normally-distributed performance metrics. We describethe changes we made to the E-divisive means algorithm along with theirmotivation. The main change we adopted was to replace the significance testusing randomized permutations with a Student's t-test, as we discovered thatthe randomized approach did not produce deterministic results, at least notwith a reasonable number of iterations. In addition we've made tweaks thatallow us to find change points the original algorithm would not, such as twonearby changes. For evaluation, we developed a method to generate realtimeseries, but with artificially injected changes in latency. We used thesedata sets to compare Hunter against two other well known algorithms, PELT andDYNP. Finally, we conclude with lessons we've learned supporting Hunter acrossteams with individual responsibility for the performance of their project.

Hunter: Using Change Point Detection to Hunt for Performance Regressions