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This artifact contains resources for reproducing and extending the work "The Effects of Computational Resources on Flaky Tests" Contents:  Analysis and Processed Test Results.tgz: An archive that contains information about the projects analyzed, summarized test results per-throttling configuration per-run, and a Jupyter notebook that detects RAFT (generating all tables and figures in the article). A README in this archive provides further guidance on its contents js-results.tar, java-results.tar, python-results.tar: The raw results produced by the test runner when executing each JavaScript, Java and Python project 300 times in each of the throttling configurations. See also: We have published docker containers that include each project that we studied, along with all of the dependenices for running the tests. These containers can be used to reproduce our results, or to extend our work by running additional tests. The containers are available at https://hub.docker.com/r/jonbell/raft/tags 

JavaScript is a single-threaded programming language, so asynchronous programming is practiced out of necessity to ensure that applications remain responsive in the presence of user input or interactions with file systems and networks. However, many JavaScript applications execute in environments that do exhibit concurrency by, e.g., interacting with multiple or concurrent servers, or by using file systems managed by operating systems that support concurrent I/O. In this paper, we demonstrate that JavaScript programmers often schedule asynchronous I/O operations suboptimally, and that reordering such operations may yield significant performance benefits. Concretely, we define a static side-effect analysis that can be used to determine how asynchronous I/O operations can be refactored so that asynchronous I/O-related requests are made as early as possible, and so that the results of these requests are awaited as late as possible. While our static analysis is potentially unsound, we have not encountered any situations where it suggested reorderings that change program behavior. We evaluate the refactoring on 20 applications that perform file- or network-related I/O. For these applications, we observe average speedups ranging between 0.99% and 53.6% for the tests that execute refactored code (8.1% on average).