Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

인공고관절의 세라믹 볼 헤드의 기계적 안정성 평가를 위한 3 차원 유한요소 해석

Today's smartphones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by carelessly programmed apps that leak important data by accident, and by malicious apps that exploit their given privileges to copy such data intentionally. While existing static taint-analysis approaches have the potential of detecting such data leaks ahead of time, all approaches for Android use a number of coarse-grain approximations that can yield high numbers of missed leaks and false alarms. In this work we thus present FlowDroid, a novel and highly precise static taint analysis for Android applications. A precise model of Android's lifecycle allows the analysis to properly handle callbacks invoked by the Android framework, while context, flow, field and object-sensitivity allows the analysis to reduce the number of false alarms. Novel on-demand algorithms help FlowDroid maintain high efficiency and precision at the same time. We also propose DroidBench, an open test suite for evaluating the effectiveness and accuracy of taint-analysis tools specifically for Android apps. As we show through a set of experiments using SecuriBench Micro, DroidBench, and a set of well-known Android test applications, FlowDroid finds a very high fraction of data leaks while keeping the rate of false positives low. On DroidBench, FlowDroid achieves 93% recall and 86% precision, greatly outperforming the commercial tools IBM AppScan Source and Fortify SCA. FlowDroid successfully finds leaks in a subset of 500 apps from Google Play and about 1,000 malware apps from the VirusShare project.

/pdf/flowdroid-precise-context-flow-field-object-sensitive-and-303w6s31hk.pdf

FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps

[서평]「The Unified Modeling Language User Guide」

Android provides third-party applications with an extensive API that includes access to phone hardware, settings, and user data. Access to privacy- and security-relevant parts of the API is controlled with an install-time application permission system. We study Android applications to determine whether Android developers follow least privilege with their permission requests. We built Stowaway, a tool that detects overprivilege in compiled Android applications. Stowaway determines the set of API calls that an application uses and then maps those API calls to permissions. We used automated testing tools on the Android API in order to build the permission map that is necessary for detecting overprivilege. We apply Stowaway to a set of 940 applications and find that about one-third are overprivileged. We investigate the causes of overprivilege and find evidence that developers are trying to follow least privilege but sometimes fail due to insufficient API documentation.

/pdf/android-permissions-demystified-2njulj9txr.pdf

Android permissions demystified

The goal of points-to analysis for Java is to determine the set of objects pointed to by a reference variable or a reference object field. We present object sensitivity, a new form of context sensitivity for flow-insensitive points-to analysis for Java. The key idea of our approach is to analyze a method separately for each of the object names that represent run-time objects on which this method may be invoked. To ensure flexibility and practicality, we propose a parameterization framework that allows analysis designers to control the tradeoffs between cost and precision in the object-sensitive analysis.Side-effect analysis determines the memory locations that may be modified by the execution of a program statement. Def-use analysis identifies pairs of statements that set the value of a memory location and subsequently use that value. The information computed by such analyses has a wide variety of uses in compilers and software tools. This work proposes new versions of these analyses that are based on object-sensitive points-to analysis.We have implemented two instantiations of our parameterized object-sensitive points-to analysis. On a set of 23 Java programs, our experiments show that these analyses have comparable cost to a context-insensitive points-to analysis for Java which is based on Andersen's analysis for C. Our results also show that object sensitivity significantly improves the precision of side-effect analysis and call graph construction, compared to (1) context-insensitive analysis, and (2) context-sensitive points-to analysis that models context using the invoking call site. These experiments demonstrate that object-sensitive analyses can achieve substantial precision improvement, while at the same time remaining efficient and practical.

http://www.cs.rpi.edu/~milanova/docs/tosem_submission.pdf

Parameterized object sensitivity for points-to analysis for Java

Performance optimization of stencil computations has been widely studied in the literature, since they occur in many computationally intensive scientific and engineering applications. Compiler frameworks have also been developed that can transform sequential stencil codes for optimization of data locality and parallelism. However, loop skewing is typically required in order to tile stencil codes along the time dimension, resulting in load imbalance in pipelined parallel execution of the tiles. In this paper, we develop an approach for automatic parallelization of stencil codes, that explicitly addresses the issue of load-balanced execution of tiles. Experimental results are provided that demonstrate the effectiveness of the approach.

/pdf/effective-automatic-parallelization-of-stencil-computations-gz3sewrxec.pdf

Effective automatic parallelization of stencil computations

GPUs are a class of specialized parallel architectures with tremendous computational power. The new Compute Unified Device Architecture (CUDA) programming model from NVIDIA facilitates programming of general purpose applications on their GPUs. However, manual development of high-performance parallel code for GPUs is still very challenging. In this paper, a number of issues are addressed towards the goal of developing a compiler framework for automatic parallelization and performance optimization of affine loop nests on GPGPUs: 1) approach to program transformation for efficient data access from GPU global memory, using a polyhedral compiler model of data dependence abstraction and program transformation; 2) determination of optimal padding factors for conflict-minimal data access from GPU shared memory; and 3) model-driven empirical search to determine optimal parameters for unrolling and tiling. Experimental results on a number of kernels demonstrate the effectiveness of the compiler optimization approaches developed.

A compiler framework for optimization of affine loop nests for gpgpus

The polyhedral model provides powerful abstractions to optimize loop nests with regular accesses. Affine transformations in this model capture a complex sequence of execution-reordering loop transformations that can improve performance by parallelization as well as locality enhancement. Although a significant body of research has addressed affine scheduling and partitioning, the problem of automaticallyfinding good affine transforms forcommunication-optimized coarsegrained parallelization together with locality optimization for the general case of arbitrarily-nested loop sequences remains a challenging problem.

We propose an automatic transformation framework to optimize arbitrarilynested loop sequences with affine dependences for parallelism and locality simultaneously. The approach finds good tiling hyperplanes by embedding a powerful and versatile cost function into an Integer Linear Programming formulation. These tiling hyperplanes are used for communication-minimized coarse-grained parallelization as well as for locality optimization. The approach enables the minimization of inter-tile communication volume in the processor space, and minimization of reuse distances for local execution at each node. Programs requiring one-dimensional versusmulti-dimensional time schedules (with scheduling-based approaches) are all handled with the same algorithm. Synchronization-free parallelism, permutable loops or pipelined parallelismat various levels can be detected. Preliminary studies of the framework show promising results.

/pdf/automatic-transformations-for-communication-minimized-1tet7msgri.pdf

Automatic transformations for communication-minimized parallelization and locality optimization in the polyhedral model

The goal of points-to analysis for Java is to determine the set of objects pointed to by a reference variable or a reference objet field. Improving the precision of practical points-to analysis is important because points-to information has a wide variety of client applications in optimizing compilers and software engineering tools. In this paper we present object sensitivity, a new form of context sensitivity for flow-insensitive points-to analysis for Java. The key idea of our approach is to analyze a method separately for each of the objects on which this method is invoked. To ensure flexibility and practicality, we propose a parameterization framework that allows analysis designers to control the tradeoffs between cost and precision in the object-sensitive analysis.Side-effect analysis determines the memory locations that may be modified by the execution of a program statement. This information is needed for various compiler optimizations and software engineering tools. We present a new form of side-effect analysis for Java which is based on object-sensitive points-to analysis.We have implemented one instantiation of our parameterized object-sensitive points-to analysis. We compare this instantiation with a context-insensitive points-to analysis for Java which is based on Andersen's analysis for C [4]. On a set of 23 Java programs, our experiments show that the two analyses have comparable cost. In some cases the object-sensitive analysis is actually faster than the context-insensitive analysis. Our results also show that object sensitivity significantly improves the precision of side-effect analysis, call graph construction, and virtual call resolution. These experiments demonstrate that object-sensitive analyses can achieve significantly better precision than context-insensitive ones, while at the same time remaining efficient and practical.

/pdf/parameterized-object-sensitivity-for-points-to-and-side-1i9exas766.pdf

Atanas Rountev

Papers

Parameterized object sensitivity for points-to analysis for Java

Effective automatic parallelization of stencil computations

A compiler framework for optimization of affine loop nests for gpgpus

Automatic transformations for communication-minimized parallelization and locality optimization in the polyhedral model

Parameterized object sensitivity for points-to and side-effect analyses for Java