Staged Points-to Analysis for Large Code Bases

Allen, Nicholas; Scholz, Bernhard; Krishnan, Padmanabhan

doi:10.1007/978-3-662-46663-6_7

Cited by 16 publications

(19 citation statements)

References 18 publications

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“…The check on line 13 is true because summary S is not applicable. At this point, no summary is applicable and tuple t 3 is not yet derived. The online phase is not able to make progress.…”

Section: The Online Phasementioning

confidence: 99%

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Accelerating program analyses by cross-program training

et al. 2016

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Practical programs share large modules of code. However, many program analyses are ineffective at reusing analysis results for shared code across programs. We present POLYMER, an analysis optimizer to address this problem. POLYMER runs the analysis offline on a corpus of training programs and learns analysis facts over shared code. It prunes the learnt facts to eliminate intermediate computations and then reuses these pruned facts to accelerate the analysis of other programs that share code with the training corpus.We have implemented POLYMER to accelerate analyses specified in Datalog, and apply it to optimize two analyses for Java programs: a call-graph analysis that is flow-and context-insensitive, and a points-to analysis that is flow-and context-sensitive. We evaluate the resulting analyses on ten programs from the DaCapo suite that share the JDK library. POLYMER achieves average speedups of 2.6× for the callgraph analysis and 5.2× for the points-to analysis.

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“…The check on line 13 is true because summary S is not applicable. At this point, no summary is applicable and tuple t 3 is not yet derived. The online phase is not able to make progress.…”

Section: The Online Phasementioning

confidence: 99%

“…Allen et al [3] propose a demand-and query-driven approach for points-to analysis that also involves source program transformation. They employ static program slicing and compaction to reduce the input program to a smaller program that is semantically equivalent for the points-to queries under consideration.…”

Section: Related Workmentioning

confidence: 99%

Accelerating program analyses by cross-program training

et al. 2016

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“…Here we focus only on the modeling of the unknown application towards creating a sound overapproximation of the call-graph and points-to information. Our work [ASK15] shows how to support client based analysis for large libraries.…”

mentioning

confidence: 99%

Combining type-analysis with points-to analysis for analyzing Java library source-code

Allen¹,

Krishnan²,

Scholz³

2015

Proceedings of the 4th ACM SIGPLAN International Workshop on State of the Art in Program Analysis

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The predominant work in static program analysis is focused on whole program analysis assuming that the whole program is present at analysis time and the only unknowns are program inputs. However, for library designers it is of paramount importance to perform semantic checks via static program analysis tools without the presence of an application. The literature offers only little research on partial program analysis for object-oriented programming languages including Java. Analyzing libraries statically requires novel abstractions for all possible applications that are not known a-priori.In this work, we present a static program analysis technique that reasons about the state of the library by approximating the behaviour of all possible applications. The key contribution is (1) the combination of type-analysis with points-to analysis and (2) the development of a most-general application (MGA) as a type, which represents the interaction of the library with all possible applications.

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Section: Program Compactionmentioning

confidence: 99%

“…The concept of identifying and removing the irrelevant parts has been used in other approaches to improve the efficiency of the techniques [5,64,90,20]. Allen et al [5] proposed a staged pointsto analysis framework for scaling points-to analysis to large code bases, in which the points-to analysis is performed in multiple stages. In each stage, they perform static program slicing and compaction to reduce the input program to a smaller program that is semantically equivalent for the points-to queries under consideration.…”

Section: Program Compactionmentioning

confidence: 99%

Collective program analysis

Upadhyaya

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Staged Points-to Analysis for Large Code Bases

Cited by 16 publications

References 18 publications

Accelerating program analyses by cross-program training

Accelerating program analyses by cross-program training

Combining type-analysis with points-to analysis for analyzing Java library source-code

Collective program analysis

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