# A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions

arXiv: https://arxiv.org/abs/1709.07536 · HTML: https://mlsanity.com/paper/1709.07536/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Latent Execution for Neural Program Synthesis](https://mlsanity.com/paper/2107.00101/) (2021)
- [NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction](https://mlsanity.com/paper/2203.08339/) (2022)
- [Learning to Learn to Predict Performance Regressions in Production at Meta](https://mlsanity.com/paper/2208.04351/) (2022)
- [Report of the Workshop on Program Synthesis for Scientific Computing](https://mlsanity.com/paper/2102.01687/) (2021)
- [CloudShield: Real-time Anomaly Detection in the Cloud](https://mlsanity.com/paper/2108.08977/) (2021)

## Abstract (arXiv)

The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In this paper, we apply MP to the automation of software performance regression testing. A performance regression is a software performance degradation caused by a code change. We present AutoPerf - a novel approach to automate regression testing that utilizes three core techniques: (i) zero-positive learning, (ii) autoencoders, and (iii) hardware telemetry. We demonstrate AutoPerf's generality and efficacy against 3 types of performance regressions across 10 real performance bugs in 7 benchmark and open-source programs. On average, AutoPerf exhibits 4% profiling overhead and accurately diagnoses more performance bugs than prior state-of-the-art approaches. Thus far, AutoPerf has produced no false negatives.