Solving demand versions of interprocedural analysis problems

Reps, Thomas

doi:10.1007/3-540-57877-3_26

Cited by 58 publications

(35 citation statements)

References 29 publications

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“…The description of data-flow analyses as a database query was pioneered by Ullman [32] and Reps [29], who applied Datalog's bottom-up magicset implementation to automatically derive a local implementation.…”

Section: Datalog-based Program Analysismentioning

confidence: 99%

Datalog-Based Program Analysis with BES and RWL

et al. 2011

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Alpuente Frasnedo, M.; Feliú Gabaldón, MA.; Joubert, C.; Villanueva García, A. (2011 Abstract. This paper describes two techniques for Datalog query evaluation and their application to object-oriented program analysis. The first technique transforms Datalog programs into an implicit Boolean Equation System (Bes) that can then be solved by using linear-time complexity algorithms that are available in existing, general purpose verification toolboxes such as Cadp. In order to improve scalability and to enable analyses involving advanced meta-programming features, we develop a second methodology that transforms Datalog programs into rewriting logic (Rwl) theories. This method takes advantage of the preeminent features and facilities that are available within the high-performance system Maude, which provides a very efficient implementation of Rwl. We provide evidence of the practicality of both approaches by reporting on some experiments with a number of real-world Datalog-based analyses.

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Section: Datalog-based Program Analysismentioning

confidence: 99%

Datalog-Based Program Analysis with BES and RWL

et al. 2011

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“…Demand-driven variations of the IFDS and IDE algorithms have been thoroughly studied [3,4,8,16,18]. These algorithms differ from the exhaustive algorithms in that rather than computing all nodes reachable from the start node, they determine whether a given node n is reachable.…”

Section: Related Workmentioning

confidence: 99%

Practical Extensions to the IFDS Algorithm

Naeem

Lhoták

Rodriguez

2010

Lecture Notes in Computer Science

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Abstract. This paper presents four extensions to the Interprocedural Finite Distributive Subset (IFDS) algorithm that make it applicable to a wider class of analysis problems. IFDS is a dynamic programming algorithm that implements context-sensitive flow-sensitive interprocedural dataflow analysis. The first extension constructs the nodes of the supergraph on demand as the analysis requires them, eliminating the need to build a full supergraph before the analysis. The second extension provides the procedure-return flow function with additional information about the program state before the procedure was called. The third extension improves the precision with which φ instructions are modelled when analyzing a program in SSA form. The fourth extension speeds up the algorithm on domains in which some of the dataflow facts subsume each other. These extensions are often necessary when applying the IFDS algorithm to non-separable (i.e. non-bit-vector) problems. We have found them necessary for alias set analysis and multi-object typestate analysis. In this paper, we illustrate and evaluate the extensions on a simpler problem, a variation of variable type analysis.

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“…Given a CFL-reachability formulation, a demand-driven algorithm [16] for the single-source L-path problem can be obtained automatically by applying the magic-sets transformation to L [29]. Our match edges are related to the summary edges used by the efficient CFL-reachability algorithm for balanced parentheses languages [28,31,32]. Summary edges are computed bottom-up as L-paths between parentheses are found, while match edges are added exhaustively and then refined by checking for L-paths.…”

Section: Algorithmmentioning

confidence: 99%

Refinement-based context-sensitive points-to analysis for Java

Sridharan¹,

Bodík²

2006

Proceedings of the 27th ACM SIGPLAN Conference on Programming Language Design and Implementation

211

120

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We present a scalable and precise context-sensitive points-to analysis with three key properties: (1) filtering out of unrealizable paths, (2) a context-sensitive heap abstraction, and (3) a context-sensitive call graph. Previous work [21] has shown that all three properties are important for precisely analyzing large programs, e.g., to show safety of downcasts. Existing analyses typically give up one or more of the properties for scalability.We have developed a refinement-based analysis that succeeds by simultaneously refining handling of method calls and heap accesses, allowing the analysis to precisely analyze important code while entirely skipping irrelevant code. The analysis is demanddriven and client-driven, facilitating refinement specific to each queried variable and increasing scalability. In our experimental evaluation, our analysis proved the safety of 61% more casts than one of the most precise existing analyses across a suite of large benchmarks. The analysis checked the casts in under 13 minutes per benchmark (taking less than 1 second per query) and required only 35MB of memory, far less than previous approaches.

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Solving demand versions of interprocedural analysis problems

Cited by 58 publications

References 29 publications

Datalog-Based Program Analysis with BES and RWL

Datalog-Based Program Analysis with BES and RWL

Practical Extensions to the IFDS Algorithm

Refinement-based context-sensitive points-to analysis for Java

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