RacerD: compositional static race detection

Abstract

Automatic static detection of data races is one of the most basic problems in reasoning about concurrency. We present RacerDÐa static program analysis for detecting data races in Java programs which is fast, can scale to large code, and has proven effective in an industrial software engineering scenario. To our knowledge, RacerD is the first inter-procedural, compositional data race detector which has been empirically shown to have non-trivial precision and impact. Due to its compositionality, it can analyze code changes quickly, and this allows it to perform continuous reasoning about a large, rapidly changing codebase as part of deployment within a continuous integration ecosystem. In contrast to previous static race detectors, its design favors reporting high-confidence bugs over ensuring their absence. RacerD has been in deployment for over a year at Facebook, where it has flagged over 2500 issues that have been fixed by developers before reaching production. It has been important in enabling the development of new code as well as fixing old code: it helped support the conversion of part of the main Facebook Android app from a single-threaded to a multi-threaded architecture. In this paper we describe RacerD's design, implementation, deployment and impact.

A true positives theorem for a static race detector

“…In this section we place RacerDX in the field of static race detectors that have been subjected to formal analysis. Since the majority of the static race analyzers used in industry-ThreadSafe (Atkey and Sannella 2015), Coverity's analysis suite (Chou 2014), and RacerD (Blackshear et al 2018)despite being impactful in practice, do not come with any formal theorem about their algorithm, we focus on the only two race analyzers we know of, for which a formal statement has been made: Chord (Naik et al 2006;Raghothaman et al 2018) and RacerDX. Chord is a tool which strives to be sound, or to favour reduction of false negatives over false positives.…”

“…The RacerD program analyzer searches for data races in Java programs, and it has had significantly more reported industrial impact than any other concurrency analysis that we are aware of. It was released as open source in October of 2017, and the OOPSLA'18 paper by Blackshear et al (2018) describes its design, and gives more details about its deployment. They report, for example, that over 2,500 concurrent data races found by RacerD have been fixed by Facebook developers, and that it has been used to support the conversion of Facebook's Android app rendering infrastructure from a single-threaded to a multi-threaded architecture.…”

“…A design goal was to "detect actionable races that developers find useful and respond to" but "no need to (provably) find them all". In fact, it is very easy to generate artificial false negatives, but Blackshear et al (2018) say that few have been reported in over a year of RacerD in production.One can react to this by saying that RacerD is simply an ad hoc, if effective, tool. But, the tool does not heuristically filter out bug reports in order to reduce false positives.…”

“…A design goal was to "detect actionable races that developers find useful and respond to" but "no need to (provably) find them all". In fact, it is very easy to generate artificial false negatives, but Blackshear et al (2018) say that few have been reported in over a year of RacerD in production.…”

NullAway: practical type-based null safety for Java

“…§8 showed that though NullAway is unsound, it has been able to prevent nearly all NPEs in production Android code at Uber. The RacerD system for static data race detection [19] also aims for this threshold of few to no false negatives in practice, based on bugs observed in the field. We anticipate that future work will continue this trend of static analysis and type system design based on preventing nearly all errors previously observed in testing and production, rather than aiming for strict soundness.…”