FLAVERS: A finite state verification technique for software systems

Cobleigh, Jamieson M.; Clarke, Lori A.; Osterweil, Leon J.

doi:10.1147/sj.411.0140

“…A race detection engine RacyRead : R × LVal → Bool 2. A region map Reg : PPoint → R such that RacyRead and Reg satisfy property (2). We now give two examples to illustrate the idea of race equivalence regions.…”

Section: Optimized Framework: Race Equivalence Regionsmentioning

confidence: 99%

“…These analyses can be precise, but one must painstakingly retool a new analysis for each concurrent setting. Finally, one could use model checking to infer facts by exhaustively exploring all thread interleavings [2,7,11]. While this is an extremely precise and generic approach, such analyses are unlikely to scale due to the combinatorial explosion in the number of interleavings.…”

Section: Introductionmentioning

confidence: 99%

Dataflow analysis for concurrent programs using datarace detection

Chugh

¹

,

Voung

²

,

Jhala

³

et al. 2008

Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Dataflow analyses for concurrent programs differ from their singlethreaded counterparts in that they must account for shared memory locations being overwritten by concurrent threads. Existing dataflow analysis techniques for concurrent programs typically fall at either end of a spectrum: at one end, the analysis conservatively kills facts about all data that might possibly be shared by multiple threads; at the other end, a precise thread-interleaving analysis determines which data may be shared, and thus which dataflow facts must be invalidated. The former approach can suffer from imprecision, whereas the latter does not scale.We present RADAR, a framework that automatically converts a dataflow analysis for sequential programs into one that is correct for concurrent programs. RADAR uses a race detection engine to kill the dataflow facts, generated and propagated by the sequential analysis, that become invalid due to concurrent writes. Our approach of factoring all reasoning about concurrency into a race detection engine yields two benefits. First, to obtain analyses for code using new concurrency constructs, one need only design a suitable race detection engine for the constructs. Second, it gives analysis designers an easy way to tune the scalability and precision of the overall analysis by only modifying the race detection engine. We describe the RADAR framework and its implementation using a preexisting race detection engine. We show how RADAR was used to generate a concurrent version of a null-pointer dereference analysis, and we analyze the result of running the generated concurrent analysis on several benchmarks.

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“…These techniques are required to analyze languages such as Java that include dynamic class loading. Finally, the Trace Flow Graph is used to represent concurrent programs [25]. It consists of a collection of CFGs with additional vertices and edges to represent inter-task control flow.…”

Section: Anatomy Of a Source Code Analysismentioning

confidence: 99%

Source Code Analysis: A Road Map

Binkley

¹

2007

Future of Software Engineering (FOSE '07)

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“…Model checkers explore all interleavings to verify abitrary safety properties, and so they can be used to encode dataflow analyses [25]. Flavers [2] is a finite-state property checker that employs conservative state and interleaving reductions, e.g. a may-happen-in-parallel dataflow analysis ( [18]) that conservatively prunes interleavings.…”

Section: Related Workmentioning

confidence: 99%

Dataflow analysis for concurrent programs using datarace detection

Chugh

¹

,

Voung

²

,

Jhala

³

et al. 2008

View full text Add to dashboard Cite

Dataflow analyses for concurrent programs differ from their singlethreaded counterparts in that they must account for shared memory locations being overwritten by concurrent threads. Existing dataflow analysis techniques for concurrent programs typically fall at either end of a spectrum: at one end, the analysis conservatively kills facts about all data that might possibly be shared by multiple threads; at the other end, a precise thread-interleaving analysis determines which data may be shared, and thus which dataflow facts must be invalidated. The former approach can suffer from imprecision, whereas the latter does not scale.We present RADAR, a framework that automatically converts a dataflow analysis for sequential programs into one that is correct for concurrent programs. RADAR uses a race detection engine to kill the dataflow facts, generated and propagated by the sequential analysis, that become invalid due to concurrent writes. Our approach of factoring all reasoning about concurrency into a race detection engine yields two benefits. First, to obtain analyses for code using new concurrency constructs, one need only design a suitable race detection engine for the constructs. Second, it gives analysis designers an easy way to tune the scalability and precision of the overall analysis by only modifying the race detection engine. We describe the RADAR framework and its implementation using a preexisting race detection engine. We show how RADAR was used to generate a concurrent version of a null-pointer dereference analysis, and we analyze the result of running the generated concurrent analysis on several benchmarks.

show abstract

FLAVERS: A finite state verification technique for software systems

Cited by 41 publications

References 41 publications

Dataflow analysis for concurrent programs using datarace detection

Dataflow analysis for concurrent programs using datarace detection

Source Code Analysis: A Road Map

Dataflow analysis for concurrent programs using datarace detection

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