Keeper: Automated Testing and Fixing of Machine Learning Software—RCR Report

This artifact aims to provide source code, benchmark suite, results, and materials used in our study “Keeper: Automated Testing and Fixing of Machine Learning Software” [3]. We developed an automated testing and fixing tool Keeper and its IDE plugin for ML software. It automatically detects software defects and attempts to change how ML APIs are used to alleviate software misbehavior. This artifact provides guidelines to set up and execute Keeper and also guidelines to interpret our evaluation results. We hope this artifact can motivate and help future research to further tackle ML API misuses. All related data are available online.