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LOCKSMITH: Practical static race detection for C

Authors:

Polyvios Pratikakis,

Jeffrey S. Foster,

Michael HicksAuthors Info & Claims

ACM Transactions on Programming Languages and Systems (TOPLAS), Volume 33, Issue 1

Article No.: 3, Pages 1 - 55

https://doi.org/10.1145/1889997.1890000

Published: 25 January 2011 Publication History

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Abstract

Locksmith is a static analysis tool for automatically detecting data races in C programs. In this article, we describe each of Locksmith's component analyses precisely, and present systematic measurements that isolate interesting trade-offs between precision and efficiency in each analysis. Using a benchmark suite comprising stand-alone applications and Linux device drivers totaling more than 200,000 lines of code, we found that a simple no-worklist strategy yielded the most efficient interprocedural dataflow analysis; that our sharing analysis was able to determine that most locations are thread-local, and therefore need not be protected by locks; that modeling C structs and void pointers precisely is key to both precision and efficiency; and that context sensitivity yields a much more precise analysis, though with decreased scalability. Put together, our results illuminate some of the key engineering challenges in building Locksmith and data race detection analyses in particular, and constraint-based program analyses in general.

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LOCKSMITH: Practical static race detection for C

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Reviewer: Prahladavaradan Sampath

We can judge the maturity of a field of scientific inquiry by the nature of the papers published in peer-reviewed journals. This paper indicates that the field of program analysis is reaching a level of maturity-it does not simply focus on novelty and theory, but is also concerned with the practical application of the theory, with some thought given to the repeatability of the results. This paper describes the implementation of a data race analysis for C programs. The authors describe their design decisions and back up these decisions with experimental data from the analysis of a benchmark suite of programs. The data race analysis is composed of a number of smaller analyses that are each specified as constraint satisfaction problems implemented using a dataflow framework. The design of the analyses is motivated by data collected from different experiments. In some cases, the data reinforces folklore, such as the fact that context sensitivity can improve both precision and efficiency. In Sections 4 and 5, the authors dwell in detail on many forms of context sensitivity that improve precision and efficiency. In other cases, the data is surprising: take, for example, the fact that a no-worklist strategy is apparently better than a worklist strategy. The most technically complex part of the paper is Section 6, on universal and existential context sensitivity. Here, however, the authors could have made more of an effort to explain these concepts and their application, as they did in earlier sections. Overall, this paper is an important contribution to the literature. Due to the availability of the full implementation on the authors' Web site (http://www.cs.umd.edu/projects/PL/locksmith/) and the easy availability of the benchmark suite, this paper should encourage more experimental and application-oriented work in program analysis. Online Computing Reviews Service

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cover image ACM Transactions on Programming Languages and Systems

ACM Transactions on Programming Languages and Systems Volume 33, Issue 1

January 2011

218 pages

ISSN:0164-0925

EISSN:1558-4593

DOI:10.1145/1889997

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 January 2011

Accepted: 01 February 2010

Revised: 01 October 2009

Received: 01 July 2008

Published in TOPLAS Volume 33, Issue 1

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LOCKSMITH: context-sensitive correlation analysis for race detection

LOCKSMITH: context-sensitive correlation analysis for race detection

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