Static analysis for concurrent programs with applications to data race detection

We propose a general framework for static analysis of concurrent multi-threaded programs in the presence of various types of synchronization primitives such as locks and pairwise rendezvous. In order to capture interference between threads, we use the notion of a transaction , i.e., a sequence of st...

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Vydané v:International journal on software tools for technology transfer Ročník 15; číslo 4; s. 321 - 336
Hlavní autori: Kahlon, Vineet, Sankaranarayanan, Sriram, Gupta, Aarti
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2013
Springer Nature B.V
Predmet:
Computer programs
Computer Science
Concurrent engineering
Data analysis
Graphs
Invariants
Mathematical models
Race
Software
Software Engineering
Software Engineering/Programming and Operating Systems
Static code analysis
Synchronism
Tacas 2009
Theory of Computation
Warning
Transaction graphs
Abstract interpretation
Data race detection
Multi-threaded programs
Static analysis
ISSN:1433-2779, 1433-2787
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Shrnutí:We propose a general framework for static analysis of concurrent multi-threaded programs in the presence of various types of synchronization primitives such as locks and pairwise rendezvous. In order to capture interference between threads, we use the notion of a transaction , i.e., a sequence of statements in a thread that can be executed atomically, without sacrificing the soundness of the analysis while yielding precise results. These transactions are delineated automatically, and are captured in the form of a transaction graph over the global control state space of the program. Starting from a coarse transaction graph, constructed by exploiting scheduling constraints related to synchronizations and partial order reduction, we iteratively refine the graph by removing statically unreachable nodes using the results of various analyses. Specifically, we use abstract interpretation to automatically derive program invariants, based on abstract domains of increasing precision. Progressive refinement of the transaction graph enhances scalability of the static analyses, yielding more precise invariants. We demonstrate the benefits of this framework in an application to find data race bugs in concurrent programs, where our static analyses serve to reduce the number of false warnings captured by an initial lockset analysis. This framework also facilitates use of model checking on the remaining warnings to generate concrete error traces, where we leverage the preceding static analyses to generate small program slices and the derived invariants to improve performance. We describe our experimental results on a suite of Linux device drivers.
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ISSN:1433-2779
1433-2787
DOI:10.1007/s10009-013-0274-1