2022/08/08 by Moritz Beller, Beller, Moritz, Hongyu Li +15 · 3 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering (cs.SE) #Software Engineering Research #Software System Performance and Reliability #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2208.04351
openalex publication_date 2022/08/08 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
Catching and attributing code change-induced performance regressions in production is hard; predicting them beforehand, even harder. A primer on automatically learning to predict performance regressions in software, this article gives an account of the experiences we gained when researching and deploying an ML-based regression prediction pipeline at Meta. In this paper, we report on a comparative study with four ML models of increasing complexity, from (1) code-opaque, over (2) Bag of Words, (3) off-the-shelve Transformer-based, to (4) a bespoke Transformer-based model, coined SuperPerforator. Our investigation shows the inherent difficulty of the performance prediction problem, which is characterized by a large imbalance of benign onto regressing changes. Our results also call into question the general applicability of Transformer-based architectures for performance prediction: an off-the-shelve CodeBERT-based approach had surprisingly poor performance; our highly customized SuperPerforator architecture initially achieved prediction performance that was just on par with simpler Bag of Words models, and only outperformed them for down-stream use cases. This ability of SuperPerforator to transfer to an application with few learning examples afforded an opportunity to deploy it in practice at Meta: it can act as a pre-filter to sort out changes that are unlikely to introduce a regression, truncating the space of changes to search a regression in by up to 43%, a 45x improvement over a random baseline. To gain further insight into SuperPerforator, we explored it via a series of experiments computing counterfactual explanations. These highlight which parts of a code change the model deems important, thereby validating the learned black-box model.