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Monday, February 28, 2022

"Change Point Detection (#ChangeDetection) for MongoDB Time Series Performance Regression" paper for ACM/SPEC ICPE 2022 Data Challenge Track

The ACM/SPEC ICPE 2022 - Data Challenge Track Committee has decided to ACCEPT our article:

TITLE: Change Point Detection for MongoDB Time Series Performance Regression
AUTHORS: Md Shahriar Iqbal, Mark Leznik, Igor Trubin, Arne Lochner, Pooyan Jamshidi and André Bauer

Commits to the MongoDB software repository trigger a collection
of automatically run tests. Here, the identification of commits 
responsible for performance regressions is paramount. Previously, the
process relied on manual inspection of time series graphs to identify
signi￿cant changes, later replaced with a threshold-based detection
system. However, neither system was sufficient for finding changes
in performance in a timely manner. This work describes our recent
implementation of a change point detection system built upon the
Perfomalist approach in combination with XGBoost algorithm. The
algorithm produces a list of change points representing significant
changes from a given history of performance results. We are able
to automatically detect change points and achieve an 83% accuracy,
all while reducing the human effort in the process.

More Perfomalist's  approach details can be found in this blog post:

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