Compositional pointer and escape analysis for Java programs

WhaleyJohn,; RinardMartin,

doi:10.1145/320385.320400

Cited by 160 publications

(238 citation statements)

References 25 publications

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“…These challenges make precise effect analysis unique compared to most other abstract interpretation techniques, including many shape analyses. Fortunately, they have been addressed to a large extent by Salcianu, Rinard and Whaley [26,31], whose work this paper extends. The remaining challenge lies in overcoming imprecision arising from highly dynamic method invocations, such as the ones arising from first-class function invocations.…”

Section: Overview Of Challenges and Solutionsmentioning

confidence: 86%

Effect Analysis for Programs with Callbacks

Kneuss

Kunčak

Suter

2014

Verified Software: Theories, Tools, Experiments

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We introduce a precise interprocedural effect analysis for programs with mutable state, dynamic object allocation, and dynamic dispatch. Our analysis is precise even in the presence of dynamic dispatch where the context-insensitive estimate on the number of targets is very large. This feature makes our analysis appropriate for programs that manipulate first-class functions (callbacks). We first present a framework in which programs are enriched with special effect statements, and define the semantics of both program and effect statements as relations on states. Our framework defines a program composition operator that is sound with respect to relation composition. Computing the summary of a procedure then consists of composing all its program statements to produce a single effect statement. We propose a strategy for applying the composition operator in a way that balances precision and efficiency.We instantiate this framework with a domain for tracking read and write effects, where relations on program states are abstracted as graphs. We implemented the analysis as a plugin for the Scala compiler. We analyzed the Scala standard library containing 58K methods and classified them into several categories according to their effects. Our analysis proves that over one half of all methods are pure. We also analyze how context sensitivity and composition operator application strategies impact the analysis precision and performance.

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Section: Overview Of Challenges and Solutionsmentioning

confidence: 86%

Effect Analysis for Programs with Callbacks

Kneuss

Kunčak

Suter

2014

Verified Software: Theories, Tools, Experiments

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“…However, a strong update can be applied at a store statement v. f = v 1 only under three conditions [61,82]:…”

Section: Context-sensitive Strong Updates In Points-to Analysismentioning

confidence: 99%

Decisions: Algebra and Implementation

Danylenko

Lundberg

Löwe

2011

Machine Learning and Data Mining in Pattern Recognition

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Processing decision information is a constitutive part in a number of applications in Computer Science fields. In general, decision information can be used to deduce the relationship between a certain context and a certain decision. Decision information is represented by a decision model that captures this information. Frequently used examples of decision models are decision tables and decision trees. The choice of an appropriate decision model has an impact on application performance in terms of memory consumption and execution time. High memory expenses can possibly occur due to redundancy in a decision model; and high execution time is often a consequence of an unsuitable decision model. Applications in different domains try to overcome these problems by introducing new data structures or algorithms for implementing decision models. These solutions are usually domain-specific and hard to transfer from one domain to another.Different application domains of Computer Science often process decision information in a similar way and, hence, have similar problems. We should thus be able to present a unifying approach that can be applicable in all application domains for capturing and manipulating decision information. Therefore, the goal of this thesis is (i) to suggest a general structure (Decision Algebra) which provides a common theoretical framework that captures decision information and defines operations (signatures) for storing, accessing, merging, approximating, and manipulating such information along with some general algebraic laws regardless of the used implementation. Our Decision Algebra allows defining different construction strategies for decision models and data structures that capture decision information as implementation variants, and it simplifies experimental comparisons between them.Additionally, this thesis presents (ii) an implementation of Decision Algebra capturing the information in a non-redundant way and performing the operations efficiently. In fact, we show that existing decision models that originated in the field of Data Mining and Machine Learning and variants thereof as exploited in special algorithms can be understood as alternative implementation variants of the Decision Algebra by varying the implementations of the Decision Algebra operations. Hence, this work (iii) will contribute to a classification of existing technology for processing decision information in different application domains of Computer Science.

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“…Also related are analyses which traverse the program callgraph (mostly bottom-up but some top-down analyses have also been proposed) and compute a summary function for each procedure [30,4,5]. This summary function is then used when analyzing the callers.…”

Section: Related Workmentioning

confidence: 99%

“…Escape analysis has been widely studied [8,3,1,30] and used in a variety of applications ranging from allocating objects on the stack to eliminating unnecessary synchronization in Java programs. To determine whether an object can be allocated on the stack and whether it is accessed by a single thread, Choi et al [8] compute object escape information using connected graphs.…”

Section: Related Workmentioning

confidence: 99%

Faster Alias Set Analysis Using Summaries

Naeem

Lhoták

2011

Lecture Notes in Computer Science

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Abstract. Alias sets are an increasingly used abstraction in situations which require flow-sensitive tracking of objects through different points in time and the ability to perform strong updates on individual objects. The interprocedural and flow-sensitive nature of these analyses often make them difficult to scale. In this paper, we use two types of method summaries (callee and caller) to improve the performance of an interprocedural flow-and context-sensitive alias set analysis. We present callee method summaries and algorithms to compute them. The computed summaries contain sufficient escape and return value information to selectively replace flow-sensitive analysis of methods without affecting analysis precision. When efficiency is a bigger concern, we also use caller method summaries which provide conservative initial assumptions for pointer and aliasing relations at the start of a method. Using caller summaries in conjunction with callee summaries enables the alias set analysis to flow-sensitively analyze only methods containing points of interest thereby reducing running time. We present results from empirically evaluating the use of these summaries for the alias set analysis. Additionally, we also discuss precision results from a realistic client analysis for verifying temporal safety properties. The results show that although caller summaries theoretically reduce precision, empirically they do not. Furthermore, on average, using callee and caller summaries reduces the running time of the alias set analysis by 27% and 96%, respectively.

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Compositional pointer and escape analysis for Java programs

Cited by 160 publications

References 25 publications

Effect Analysis for Programs with Callbacks

Effect Analysis for Programs with Callbacks

Decisions: Algebra and Implementation

Faster Alias Set Analysis Using Summaries

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