Reviews: A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions

This paper describes a system for detecting the source of performance regressions in source code. The idea is to measure performance counters (HPCs) at a per-function level of the code, and then when a performance regression is detected, it is localized by looking for the function with most anomalous performance counters. The anomaly detection is done by training autoencoders on the HPCs, and there is a further idea to cluster functions with similar behavior profiles to avoid the need for learning an autoencoder for every function in a large code base. This is a controversial paper because there is little methodological novelty. R1 gave the lowest score and asks whether we want to allow this kind of paper in NeurIPS, worrying that if we accept any application of ML, then NeurIPS risks becoming too broad.