Efficient and Effective Handling of Exceptions in Java Points-to Analysis

Kastrinis, George; Smaragdakis, Yannis

doi:10.1007/978-3-642-37051-9_3

Cited by 23 publications

(14 citation statements)

References 27 publications

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“…For example, for a 2obj+H analysis, the above postprocessing adds roughly 1sec to the query reporting the InsensVarPointsTo results. 9 The reason that post-processing is virtually cost-free is dual. First, post-processing only adjusts information in the final output table, and not in all other tables involved in intermediate computations.…”

Section: Methodsmentioning

confidence: 99%

Set-based pre-processing for points-to analysis

Smaragdakis

Balatsouras

Kastrinis

2013

Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages &Amp; Application

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We present set-based pre-analysis: a virtually universal optimization technique for flow-insensitive points-to analysis. Points-to analysis computes a static abstraction of how object values flow through a program's variables. Set-based pre-analysis relies on the observation that much of this reasoning can take place at the set level rather than the value level. Computing constraints at the set level results in significant optimization opportunities: we can rewrite the input program into a simplified form with the same essential points-to properties. This rewrite results in removing both local variables and instructions, thus simplifying the subsequent value-based points-to computation. Effectively, setbased pre-analysis puts the program in a normal form optimized for points-to analysis.Compared to other techniques for off-line optimization of points-to analyses in the literature, the new elements of our approach are the ability to eliminate statements, and not just variables, as well as its modularity: set-based pre-analysis can be performed on the input just once, e.g., allowing the pre-optimization of libraries that are subsequently reused many times and for different analyses. In experiments with Java programs, set-based pre-analysis eliminates 30% of the program's local variables and 30% or more of computed context-sensitive points-to facts, over a wide set of benchmarks and analyses, resulting in a ∼20% average speedup (max: 110%, median: 18%).

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Section: Methodsmentioning

confidence: 99%

Set-based pre-processing for points-to analysis

Smaragdakis

Balatsouras

Kastrinis

2013

Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages &Amp; Application

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“…Kastrinis and Smaragdakis have recently presented a novel and particularly efficient approach for analyzing exceptions and points-to analysis in combination [18]. It would be interesting to see whether FLOWDROID can easily benefit from an integration of some of those concepts.…”

Section: Related Workmentioning

confidence: 98%

FlowDroid

Arzt

Rasthofer

Fritz

et al. 2014

Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation

735

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“…We demonstrate introspective context-sensitivity via incremental changes to an existing model for context-sensitive, flowinsensitive 1 points-to analysis algorithms [11,12]. The logical formalism of this model is very close to the core components of our actual analysis implementation.…”

Section: Base Model For Introspective Context-sensitivitymentioning

confidence: 96%

Introspective analysis

Smaragdakis

Kastrinis

Balatsouras

2014

Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Context-sensitivity is the primary approach for adding more precision to a points-to analysis, while hopefully also maintaining scalability. An oft-reported problem with context-sensitive analyses, however, is that they are bi-modal: either the analysis is precise enough that it manipulates only manageable sets of data, and thus scales impressively well, or the analysis gets quickly derailed at the first sign of imprecision and becomes orders-of-magnitude more expensive than would be expected given the program's size. There is currently no approach that makes precise context-sensitive analyses (of any flavor: call-site-, object-, or type-sensitive) scale across the board at a level comparable to that of a context-insensitive analysis. To address this issue, we propose introspective analysis: a technique for uniformly scaling context-sensitive analysis by eliminating its performance-detrimental behavior, at a small precision expense. Introspective analysis consists of a common adaptivity pattern: first perform a context-insensitive analysis, then use the results to selectively refine (i.e., analyze context-sensitively) program elements that will not cause explosion in the running time or space. The technical challenge is to appropriately identify such program elements. We show that a simple but principled approach can be remarkably effective, achieving scalability (often with dramatic speedup) for benchmarks previously completely out-of-reach for deep context-sensitive analyses.

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Efficient and Effective Handling of Exceptions in Java Points-to Analysis

Cited by 23 publications

References 27 publications

Set-based pre-processing for points-to analysis

Set-based pre-processing for points-to analysis

FlowDroid

Introspective analysis

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