As JavaScript has become virtually omnipresent as the lan- guage for programming large and complex web applica- tions in the last several years, we have seen an increase in interest in finding data races in client-side JavaScript. While JavaScript execution is single-threaded, there is still enough potential for data races, created largely by the non- determinism of the scheduler. Recently, several academic efforts have explored both static and runtime analysis ap- proaches in an effort to find data races. However, despite this, we have not seen these analysis techniques deployed in practice and we have only seen scarce evidence that devel- opers find and fix bugs related to data races in JavaScript.

In this paper we argue for a different formulation of what it means to have a data race in a JavaScript application and distinguish between benign and harmful races, affecting per- sistent browser or server state. We further argue that while benign races — the subject of the majority of prior work — do exist, harmful races are exceedingly rare in practice (19 harmful vs. 621 benign). Our results shed a new light on the issues of data race prevalence and importance.

To find races, we also propose a novel lightweight runtime symbolic exploration algorithm for finding races in traces of runtime execution. Our algorithm eschews schedule explo- ration in favor of smaller runtime overheads and thus can be used by beta testers or in crowd-sourced testing. In our experiments on 26 sites, we demonstrate that benign races are considerably more common than harmful ones.