Automatic static detection of data races is one of the most basic problems in reasoning about concurrency. We present RacerD—a 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 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 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.
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Joscha Drechsler Technische Universität Darmstadt, Ragnar Mogk Technische Universität Darmstadt, Guido Salvaneschi TU Darmstadt, Mira Mezini TU DarmstadtDOI Pre-print File Attached
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Umang Mathur University of Illinois at Urbana-Champaign, Dileep Kini University of Illinois at Urbana-Champaign, Mahesh Viswanathan University of Illinois at Urbana-ChampaignDOI Authorizer link Pre-print
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