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; even the highly customized SuperPerforator architecture achieved offline results that were on par with simpler Bag of Words models; it only started to significantly outperform it for down-stream use cases in an online setting. 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 it.The 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.

At Meta, we work to incorporate privacy-by-design into all of our products and keep user information secure. We have created an ML model that detects code changes ("diffs") that have privacy-sensitive implications. At our scale of tens of thousands of engineers creating hundreds of thousands of diffs each month, we use automated tools for detecting such diffs. Inspired by recent studies on detecting defects [2, 3, 5] and security vulnerabilities [4, 6, 7], we use techniques from natural language processing to build a deep learning system for detecting privacy-sensitive code.