A. Rountev and S. Chandra. Off-line variable substitution for scaling points-to analysis. In PLDI, pages 47--56, 2000.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to improve the precision and efficiency of pointer analyses. Horwitz [28] and Das [9] provide algorithms to improve the precision of points to analyses, the latter achieving the precision of Andersen s [2] algorithm while running almost as fast as Steensgaard s algorithm [30] Rountev and Chandra [25] use a technique called variable substitution to replace a set of program variables that are guaranteed to have the same points to sets with a single variable, which reduces the size of the problem and greatly improves efficiency. Ryder et al. 26] present a flow sensitive, interprocedural ....

A. Rountev and S. Chandra. Off-line variable substitution for scaling points-to analyis. In Proceedings of the 2000.

....analyses have been developed that compute the sets of variables to which a pointer may point; these sets are called points to sets [2] Such analyses are said to perform may alias analysis of pointers [1] or simply pointer analysis. Recent work on pointer analysis has focused on efficiency [3, 4, 5], tradeoffs between efficiency and accuracy (e.g. the choice of a subset based [6] or unification based [7] analysis [8, 9] or on the cost and benefits of context sensitivity and polymorphism [10, 11] Techniques for incremental analyses have been proposed, so that small modifications of the ....

....the three algorithms the exhaustive algorithm, the two set al..gorithm and the one set al..gorithm on the problem of computing the possible functions that may be called from each call site. The benchmarks we use are described in Table 1. The first six benchmarks were obtained from the authors of [4], the last one by the authors of [5] The columns report the number of lines of preprocessed code, the number of program variables (excluding temporary variables introduced when mapping C code into primitive assignments) the number of primitive assignments, and the number of non trivial ....

A. Rountev and S. Chandra, "Off-line Variable Substitution for Scaling Points-to Analysis", PLDI 2000.

....treatment of pointers: for assignments such as p = x we need to determine what objects p could point to. This kind of aliasing analysis is commonly called points to analysis in the literature [4] The scalability of points to analysis has been a subject of intensive study over the last few years [5, 8, 21, 11, 23]. However the feasibility of building interactive tools that employ some form of sufficiently accurate pointer analysis on million line code bases is still an open question. The paper has two main contributions. The first is an archi 1 This is a substantially revised version of [16] tecture ....

....isolation and then the individual results are combined to analyze an entire codebase. The second contribution is a new algorithm for implementing dynamic transitive closure (DTC) Previous algorithms in the literature for Andersen s analysis are based on a transitively closed constraint graph e.g. [4, 10, 11, 21, 23, 22]. In contrast, our algorithm is based on a pre transitive graph i.e. we maintain the graph in a form that is not transitively closed. When information about a node is requested, we must perform a graph reachability computation (as opposed to just looking up the information at the node itself in ....

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A. Rountev and S. Chandra, "Off-line Variable Substitution for Scaling Points-to Analysis", PLDI, 2000.

....to improve the precision and efficiency of pointer analyses. Horwitz [25] and Das [10] provide algorithms to improve the precision of points to analyses, the latter achieving the precision of Andersen s [3] algorithm while running almost as fast as Steensgaard s algorithm [26] Rountev and Chandra [23] use a technique called variable substitution to replace a set of program variables that are guaranteed to have the same points to sets with a single variable, which reduces the size of the problem and greatly improves efficiency. However, none of these techniques reduce the size of the resulting ....

A. Rountev and S. Chandra. Off-line variable substitution for scaling points-to analyis. In Proceedings of the 2000 ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 47--56, Vancouver, BC, June 2000.

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Rountev, A., and Chandra, S.: 2000, `Off-line Variable Substitution for Scaling Points-to Analysis'. In: Conference on Programming Language Design and Implementation. pp. 47--56.

.... the processing function either identifies commonly misused phrases (for the diction command) or analyses sentence length and various readability measures (for the style command) 7 Related Work There is a large body of work on pointer analyses for C with varying degrees of precision and cost [11, 9, 5, 1, 22,20,25,18,13,7,4,6,3,17]. Analysisprecisionis typically evaluated with respect to the disambiguation of indirect memory accesses (e.g. in p=1) The most frequently used metric is the average size of a points to set [20, 18, 13, 7, 4, 6] Our work evaluates pointer analysis precision with respect to the disambiguation ....

A. Rountev and S. Chandra. Off-line variable substitution for scaling points-to analysis. In Conference on pages 47--56, 2000.

....from above, pointer analysis can determine what are the locations that p may point to. In addition, pointer analysis determines which function addresses may be stored in a given function pointer. Because of the importance of such points to information, a variety of analyses have been developed [10, 9, 5, 1, 18, 17, 20, 15, 11, 7, 4, 6, 3, 14, 8]. These analyses provide different tradeoffs between cost and precision. For example, flow and context insensitive pointer analyses [1, 17, 20, 15, 4] ignore the flow of control between program points and do not distinguish between different calling contexts of procedures. As a result, such ....

....FA analysis may provide sufficient precision for the purposes of call graph construction. In this context, the use of more expensive pointer analyses may not be necessary. 6 Related Work There is a large body of work on various pointer analyses for C with different degrees of cost and precision [10, 9, 5, 1, 18, 17, 20, 15, 11, 7, 4, 6, 3, 14, 8]. Traditionally, the precision of these analyses has been evaluated with respect to the disambiguation of indirect memory reads and writes (e.g. in p=1) Our work evaluates the precision of pointer analysis with respect to indirect procedure calls and call graph construction. Existing work ....

A. Rountev and S. Chandra. Off-line variable substitution for scaling points-to analysis. In Conference on pages 47--56, 2000.

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A. Rountev and S. Chandra. Off-line variable substitution for scaling points-to analysis. In PLDI, pages 47--56, 2000.

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Atanas Rountev and Satish Chandra. Off-line Variable Substitution for Scaling Points-to Analysis. In Proceedings of PLDI'00, pages 47 -- 56, Jun 2000.

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Atanas Rountev and Satish Chandra. Off-line Variable Substitution for Scaling Points-to Analysis. In Proceedings of PLDI'00, pages 47 -- 56, Jun 2000.

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A. Rountev and S. Chandra. Off-line variable substitution for scaling points-to analysis. In Proceedings of the SIGPLAN 2000.

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