No abstract
In this paper we propose Recon, a new general approach to concurrency debugging. Recon goes beyond just detecting bugs, it also presents to the programmer short fragments of buggy execution schedules that illustrate how and why bugs happened. These fragments, called reconstructions, are inferred from inter-thread communication surrounding the root cause of a bug and significantly simplify the process of understanding bugs.The key idea in Recon is to monitor executions and build graphs that encode inter-thread communication with enough context information to build reconstructions. Recon leverages reconstructions built from multiple application executions and uses machine learning to identify which ones illustrate the root cause of a bug. Recon's approach is general because it does not rely on heuristics specific to any type of bug, application, or programming-model. Therefore, it is able to deal with single-and multiple-variable concurrency bugs regardless of their type (e.g., atomicity violation, ordering, etc). To make graph collection efficient, Recon employs selective monitoring and allows metadata information to be imprecise without compromising accuracy. With these optimizations, Recon's graph collection imposes overheads typically between 5x and 20x for both C/C++ and Java programs, with overheads as low as 13% in our experiments. We evaluate Recon with buggy applications, and show it produces reconstructions that include all code points involved in bugs' causes, and presents them in an accurate order. We include a case study of understanding and fixing a previously unresolved bug to showcase Recon's effectiveness.
To detect data races that harm production systems, program analysis must target production runs. However, sound and precise data race detection adds too much run-time overhead for use in production systems. Even existing approaches that provide soundness or precision incur significant limitations.This work addresses the need for soundness (no missed races) and precision (no false races) by introducing novel, efficient production-time analyses that address each need separately. (1) Precise data race detection is useful for developers, who want to fix bugs but loathe false positives. We introduce a precise analysis called RaceChaser that provides low, bounded run-time overhead.(2) Sound race detection benefits analyses and tools whose correctness relies on knowledge of all potential data races. We present a sound, efficient approach called Caper that combines static and dynamic analysis to catch all data races in observed runs. Race-Chaser and Caper are useful not only on their own; we introduce a framework that combines these analyses, using Caper as a sound filter for precise data race detection by RaceChaser.Our evaluation shows that RaceChaser and Caper are efficient and effective, and compare favorably with existing state-of-the-art approaches. These results suggest that RaceChaser and Caper enable practical data race detection that is precise and sound, respectively, ultimately leading to more reliable software systems.
In this paper we propose Recon, a new general approach to concurrency debugging. Recon goes beyond just detecting bugs, it also presents to the programmer short fragments of buggy execution schedules that illustrate how and why bugs happened. These fragments, called reconstructions, are inferred from inter-thread communication surrounding the root cause of a bug and significantly simplify the process of understanding bugs.The key idea in Recon is to monitor executions and build graphs that encode inter-thread communication with enough context information to build reconstructions. Recon leverages reconstructions built from multiple application executions and uses machine learning to identify which ones illustrate the root cause of a bug. Recon's approach is general because it does not rely on heuristics specific to any type of bug, application, or programming-model. Therefore, it is able to deal with single-and multiple-variable concurrency bugs regardless of their type (e.g., atomicity violation, ordering, etc). To make graph collection efficient, Recon employs selective monitoring and allows metadata information to be imprecise without compromising accuracy. With these optimizations, Recon's graph collection imposes overheads typically between 5x and 20x for both C/C++ and Java programs, with overheads as low as 13% in our experiments. We evaluate Recon with buggy applications, and show it produces reconstructions that include all code points involved in bugs' causes, and presents them in an accurate order. We include a case study of understanding and fixing a previously unresolved bug to showcase Recon's effectiveness.
Researchers have proposed always-on data-race exceptions as a way to avoid the ill effects of data races, but slow performance of accurate dynamic data-race detection remains a barrier to the adoption of always-on data-race exceptions. Proposals for accurate low-level (e.g., hardware) data-race detection have the potential to reduce this performance barrier. This paper explains why low-level data-race detectors are wrong for programs written in high-level languages (e.g., Java): they miss true data races and report false data races in these programs. To bring the benefits of low-level datarace detection to high-level languages, we design low-level abstractable race detection (LARD), an extension of the interface between low-level data-race detectors and run-time systems that enables accurate language-level data-race detection using low-level detection mechanisms. We implement accurate LARD data-race exception support for Java, coupling a modified Jikes RVM Java virtual machine and a simulated hardware race detector. We evaluate our detector's accuracy against an accurate dynamic Java data-race detector and other low-level race detectors without LARD, showing that naïve accurate low-level data-race detectors suffer from many missed and false language-level races in practice, and that LARD prevents this inaccuracy.
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