Thu 8 Nov 2018 13:52 - 14:15 at Studio 1 - Parallelism and Correctness Chair(s): Werner Dietl

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.

Thu 8 Nov

Displayed time zone: Guadalajara, Mexico City, Monterrey change

13:30 - 15:00
Parallelism and CorrectnessOOPSLA at Studio 1
Chair(s): Werner Dietl University of Waterloo, Canada
13:30
22m
Talk
Thread-Safe Reactive Programming
OOPSLA
Joscha Drechsler Technische Universität Darmstadt, Ragnar Mogk Technische Universität Darmstadt, Guido Salvaneschi TU Darmstadt, Mira Mezini TU Darmstadt
DOI Pre-print File Attached
13:52
22m
Talk
RacerD: Compositional Static Race Detection
OOPSLA
Sam Blackshear Facebook, Nikos Gorogiannis , Peter W. O'Hearn Facebook and University College London, Ilya Sergey Yale-NUS College
Pre-print
14:15
22m
Talk
What Happens-After the First Race? Enhancing the Predictive Power of Happens-Before Based Dynamic Race Detection
OOPSLA
Umang Mathur University of Illinois at Urbana-Champaign, Dileep Kini University of Illinois at Urbana-Champaign, Mahesh Viswanathan University of Illinois at Urbana-Champaign
DOI Authorizer link Pre-print
14:37
22m
Talk
Sound Deadlock Prediction
OOPSLA
Christian Gram Kalhauge University of California, Los Angeles, Jens Palsberg University of California, Los Angeles