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.

The increasing number of software applications incorporating machine learning (ML) solutions has led to the need for testing techniques. However, testing ML software requires tremendous human effort to design realistic and relevant test inputs and to judge software output correctness according to human common sense. Even when misbehavior is exposed, it is often unclear whether the defect is inside ML API or the surrounding code and how to fix the implementation. This article tackles these challenges by proposing Keeper, an automated testing and fixing tool for ML software. The core idea of Keeper is designing pseudo-inverse functions that semantically reverse the corresponding ML task in an empirical way and proxy common human judgment of real-world data. It incorporates these functions into a symbolic execution engine to generate tests. Keeper also detects code smells that degrade software performance. Once misbehavior is exposed, Keeper attempts to change how ML APIs are used to alleviate the misbehavior. Our evaluation on a variety of applications shows that Keeper greatly improves branch coverage, while identifying 74 previously unknown failures and 19 code smells from 56 out of 104 applications. Our user studies show that 78% of end-users and 95% of developers agree with Keeper’s detection and fixing results.

An increasing number of software applications incorporate machine learning (ML) solutions for cognitive tasks that statistically mimic human behaviors. To test such software, tremendous human effort is needed to design image/text/audio inputs that are relevant to the software, and to judge whether the software is processing these inputs as most human beings do. Even when misbehavior is exposed, it is often unclear whether the culprit is inside the cognitive ML API or the code using the API.

Modern web applications manipulate a large amount of user data and undergo frequent data-schema changes. These changes bring up a unique refactoring task: updating application code to be consistent with data schema. Previous study and our own investigation show that this type of refactoring is error-prone and time-consuming for developers. This paper presents EvolutionSaver, a static code analysis and transformation tool that automates schema-related code refactoring and consistency checking. EvolutionSaver is implemented as an IDE plugin that works for both Rails and Django applications. The source code of EvolutionSaver is available on Github [1] and the plugin can be downloaded from Visual Studio Marketplace [2], with its tutorial available at https://www.youtube.com/watch?v=qBiMkLFIjbE and DOI 10.5281/zenodo.5276127.