SmartTrack: Efficient Predictive Race Detection

Widely used data race detectors, including the state-of-the-art FastTrack algorithm, incur performance costs that are acceptable for regular in-house testing, but miss races detectable from the analyzed execution. Predictive analyses detect more data races in an analyzed execution than FastTrack detects, but at significantly higher performance cost. This paper presents SmartTrack, an algorithm that optimizes predictive race detection analyses, including two analyses from prior work and a new analysis introduced in this paper. SmartTrack's algorithm incorporates two main optimizations: (1) epoch and ownership optimizations from prior work, applied to predictive analysis for the first time; and (2) novel conflicting critical section optimizations introduced by this paper. Our evaluation shows that SmartTrack achieves performance competitive with FastTrack-a qualitative improvement in the state of the art for data race detection.Comment: Extended arXiv version of PLDI 2020 paper (adds Appendices A-E) #228 SmartTrack: Efficient Predictive Race Detectio

Optimal Stateless Model Checking under the Release-Acquire Semantics

We present a framework for the efficient application of stateless model checking (SMC) to concurrent programs running under the Release-Acquire (RA) fragment of the C/C++11 memory model. Our approach is based on exploring the possible program orders, which define the order in which instructions of a thread are executed, and read-from relations, which specify how reads obtain their values from writes. This is in contrast to previous approaches, which also explore the possible coherence orders, i.e., orderings between conflicting writes. Since unexpected test results such as program crashes or assertion violations depend only on the read-from relation, we avoid a potentially significant source of redundancy. Our framework is based on a novel technique for determining whether a particular read-from relation is feasible under the RA semantics. We define an SMC algorithm which is provably optimal in the sense that it explores each program order and read-from relation exactly once. This optimality result is strictly stronger than previous analogous optimality results, which also take coherence order into account. We have implemented our framework in the tool Tracer. Experiments show that Tracer can be significantly faster than state-of-the-art tools that can handle the RA semantics.Comment: Accepted paper in OOPSLA'1

Data Races vs. Data Race Bugs: Telling the Difference with Portend

Even though most data races are harmless, the harmful ones are at the heart of some of the worst concurrency bugs. Alas, spotting just the harmful data races in programs is like finding a needle in a haystack: 76%-90% of the true data races reported by state-of-the- art race detectors turn out to be harmless [45]. We present Portend, a tool that not only detects races but also automatically classifies them based on their potential con- sequences: Could they lead to crashes or hangs? Could their effects be visible outside the program? Are they harmless? Our proposed technique achieves high accuracy by efficiently analyzing multiple paths and multiple thread schedules in combination, and by performing symbolic comparison between program outputs. We ran Portend on 7 real-world applications: it detected 93 true data races and correctly classified 92 of them, with no human effort. 6 of them are harmful races. Portend’s classification accuracy is up to 88% higher than that of existing tools, and it produces easy- to-understand evidence of the consequences of harmful races, thus both proving their harmfulness and making debugging easier. We envision Portend being used for testing and debugging, as well as for automatically triaging bug reports

Accurately Classifying Data Races with Portend

Even though most data races are harmless, the harmful ones are at the heart of some of the worst concurrency bugs. Eliminating all data races from programs is impractical (e.g., system performance could suffer severely), yet spotting just the harmful ones is like finding a needle in a haystack: state-of-the-art data race detectors and classifiers suffer from high false positive rates of 37%–84%. We present Portend, a technique and system for automatically triaging suspect data races based on their potential consequences: Could they lead to crashes or hangs? Alter system state? Could their effects be externalized? Or are they harmless? Our proposed technique achieves very high accuracy by efficiently analyzing multiple paths and multiple thread schedules in combination, and by performing symbolic comparison between program states. We ran Portend on several dozen data races from real-world applications, and it correctly classified all of them, with no human effort. It also produced easy-to-understand evidence of the consequences of harmful races, thus proving their harmfulness and making debugging easier. We envision using Portend for testing and debugging, as well as for automatically triaging bug reports