Summary-Based Context-Sensitive Data-Dependence Analysis in Presence of Callbacks

Tang, Hao; Wang, Xiaoyin; Zhang, Lingming; Xie, Bing; Zhang, Lu; Mei, Hong

doi:10.1145/2676726.2676997

Cited by 36 publications

(15 citation statements)

References 49 publications

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“…Second, it offers fast client-code analysis, since the expensive cost of analyzing the huge libraries has been spent offline, in an earlier stage. Data-dependence analysis admits a nice separation between library and client code, and has been studied in [Palepu et al 2017;Tang et al 2015]. • The challenge of callbacks.…”

Section: Introductionmentioning

confidence: 99%

“…• The challenge of callbacks. As pointed out recently in [Tang et al 2015], one major obstacle to effective library summarization is the presence of callbacks. Callback functions are declared and used by the library, but are implemented by the client.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Library/Client data-dependence analysis. The standard algorithmic formulation of contextsensitive data-dependence analysis is via Dyck reachability, where the parenthesis are used to properly match method calls and returns in a context-sensitive way [Reps 2000;Tang et al 2015]. The algorithmic approach to Library/Client Dyck reachability consists of considering two graphs G 1 and G 2 , for the library and client code respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In the presence of callbacks, library summarization via CFL reachability is ineffective, as n ′ 1 can be as large as n 1 . To face this challenge, the recent work of [Tang et al 2015] introduced TAL reachability. This approach spends O(n 6 1 ) time on the client code (hence more than the CFL reachability algorithm), and is able to produce a summary of size s < n 1 even in the presence of callbacks.…”

Section: Introductionmentioning

confidence: 99%

“…Library/Client CFL reachability on the library graph of size n 1 and the client graph of size n 2 . s is the number of library summary nodes, as defined in [Tang et al 2015]. k 1 is the number of call sites in the library code, with k 1 < s. k 2 is the number of call sites in the client code.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Dyck reachability for data-dependence and alias analysis

Chatterjee

Choudhary

Pavlogiannis

2017

Proc. ACM Program. Lang.

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A fundamental algorithmic problem at the heart of static analysis is Dyck reachability. The input is a graph where the edges are labeled with different types of opening and closing parentheses, and the reachability information is computed via paths whose parentheses are properly matched. We present new results for Dyck reachability problems with applications to alias analysis and data-dependence analysis. Our main contributions, that include improved upper bounds as well as lower bounds that establish optimality guarantees, are as follows. First, we consider Dyck reachability on bidirected graphs, which is the standard way of performing fieldsensitive points-to analysis. Given a bidirected graph with n nodes and m edges, we present: (i) an algorithm with worst-case running time O(m + n · α(n)), where α(n) is the inverse Ackermann function, improving the previously known O(n 2 ) time bound; (ii) a matching lower bound that shows that our algorithm is optimal wrt to worst-case complexity; and (iii) an optimal average-case upper bound of O(m) time, improving the previously known O(m · log n) bound. Second, we consider the problem of context-sensitive data-dependence analysis, where the task is to obtain analysis summaries of library code in the presence of callbacks. Our algorithm preprocesses libraries in almost linear time, after which the contribution of the library in the complexity of the client analysis is only linear, and only wrt the number of call sites. Third, we prove that combinatorial algorithms for Dyck reachability on general graphs with truly subcubic bounds cannot be obtained without obtaining sub-cubic combinatorial algorithms for Boolean Matrix Multiplication, which is a long-standing open problem. Thus we establish that the existing combinatorial algorithms for Dyck reachability are (conditionally) optimal for general graphs. We also show that the same hardness holds for graphs of constant treewidth. Finally, we provide a prototype implementation of our algorithms for both alias analysis and data-dependence analysis. Our experimental evaluation demonstrates that the new algorithms significantly outperform all existing methods on the two problems, over real-world benchmarks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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