Dave—a validation error detection and documentation system for fortran programs

This paper describes DAVE, a system for analysing Fortran programs. DAVE is capable of detecting the symptoms of a wide variety of errors In programs, as well as assuring the absence of these errors. In addition, DAVE exposes and documents subtle data relations and flows within programs. The central analytic procedure used is a depth first search. DAVE itself is written in Fortran. Its implementation at the University of Colorado and some early experience are described.     

Prtds — a pascal run-time diagnostics system

A run-time diagnostics system has been described that allows the user to interrogate a program fully in source language terms. In particular, dynamically created data structures are catered for. The diagnostics system allows all objects including those created dynamically to be interrogated and can display a graphical representation of linked data structures. This paper describes the implementation of such a system with the language Pascal on a GEC 4080.     

Anomaly detection in concurrent Java programs using dynamic data flow analysis

Concurrency constructs are widely used when developing complex software such as real-time, networking and multithreaded client–server applications. Consequently, testing a program, which includes concurrency constructs is a very elaborate and complex process. In this work, we first identify the different classes of synchronization anomalies that may occur in concurrent Java programs. We then consider testing concurrent Java programs against synchronization anomalies using dynamic data flow analysis techniques. Moreover, we show how the data flow analysis technique can be extended to detect such anomalies.     

A type and effect system for activation flow of components in Android programs

This paper proposes a type and effect system for analyzing activation flow between components through intents in Android programs. The activation flow information is necessary for all Android analyses such as a secure information flow analysis for Android programs. We first design a formal semantics for a core of featherweight Android/Java, which can address interaction between components through intents. Based on the formal semantics, we design a type and effect system for analyzing activation flow between components and demonstrate the soundness of the system.     

基于程序插装的动态测试技术实现

动态测试是检测程序的动态行为的一种有效手段,本文介绍了Safepro白盒软件测试工具系列中基于程序插装的动脉测试技术实现,具体讨论了动态测试的模型、数据流模型和动态跟踪数据的编码和解码技术,插装库设计与插装策略等内容。     

A sentence generator for a compiler for PT,a pascal subset

An implementation of a sentence generator system for a compiler for PT, a subset of Pascal, is discussed. Using the syntax definition of PT, the system automatically produces leftmost generations in a pseudorandom fashion. Syntactically correct as well as compile-time correct programs can be generated. In the generation of compile-time correct programs, only those statements that satisfy the contextual constraints imposed by the declarative part of the generated program are generated. The system also provides the facility of producing programs that have lexical or syntactic errors. All the generated programs are printed with appropriate indentations to show structural properties. Furthermore, the number of procedures in the generated program, the number of statements, identifier length and the number of generations can be controlled. The implementation has been successfully used for testing an error recovery parser as well as an intermediate code generator for PT.     

A complexity measure for data flow models

For common data flow schemes, the number of copies of tokens made during a computation is shown to be a Blum complexity measure.⁽¹⁾ Results from abstract complexity theory (see Ref. 2) then hold for the copy measure, indicating, for example, that any implementation of a data flow processor will be constrained by its ability to copy tokens. The copy measure is a natural measure of complexity for data flow computations, and is distinct from the usual time or space measures. The result is generalized to a wider class of data flow schemas, including those with an apply operator. An example is also given of a data flow scheme which makes no copies.Supported in part by NSF Grant MCS 83-01536 and NSA OCREAE Grant MDA904-85-H-0002.     

Effectively exploiting parallelism in data flow analysis

We present an effective approach to performing data flow analysis in parallel and identify three types of parallelism inherent in this solution process: independent-problem parallelism, separate-unit parallelism and algorithmic parallelism. We present our investigations of Fortran procedures from thePerfect Benchmarks andnetlib libraries, which reveal structural characteristics of program flow graphs that are amenable to algorithmic parallelism. Previously, the utility of algorithmic parallelism had been explored using our parallel hybrid algorithm in the context of solving the Reaching Definitions problem for Fortran procedures. Here we present new refinements that optimize performance by increasing the grain size of the parallelism, to improve communication on distributed-memory machines. The empirical performance of our optimized and unoptimized hybrid algorithms for Reaching Definitions are compared on this large data set using an iPSC/2. Our empirical findings qualitatively validate the usefulness of algorithmic parallelism.This research was supported, in part, by National Science Foundation grants CCR-8920078 and CCR-9023628-1, 2/5. An earlier version of this paper appears inProceedings of the 6th ACM International Conference on Supecomputing (Washington, D.C., July 1992), pp. 236–247.     

A review of design techniques for programs and data

The proliferation of papers on programming methodology focus on the program development process but only hint at the form of the final program. This paper distinguishes between the development process and the program product and presents a catalogue of possible program organizations and data structures with examples drawn from the published literature. The methods for sharing data among modules and a classification scheme for programs and data structures is presented.     

A structural testing method for JSP designed programs

A new structural testing method (STM) is presented for use with JSP (Jackson structured programming) designed programs. The method involves the creation of a path expression from the program structure and subsequent expansion of the expression into a set of paths using an algebra described in the paper. The treatment of each JSP construct is dealt with in detail. A worked example demonstrates the use of the method to generate test cases. The method is compared in terms of coverage to other well-known structural testing methods. The advantages and disadvantages of the method are discussed and further work currently being undertaken by the authors is outlined.     

Detecting loop-carried dependences in programs with dynamic data structures

The problem of data dependences detection in codes based on dynamic data structures, is crucial to various compiler optimizations. The approach presented in this paper focuses on detecting data dependences induced by heap-directed pointers on loops that access dynamic data structures. Knowledge about the shape of the data structure accessible from a heap-directed pointer provides critical information for disambiguating heap accesses originating from it. The new approach is based on a previously developed shape analysis that maintains topological information of the connections among the different nodes (memory locations) in the data structure. As a novelty, our approach carries out abstract interpretation of the statements being analyzed, annotating memory locations with read/write information. This information will be later used in a very accurate data dependence test which we describe in this paper. We also discuss its application to several different benchmarks.     

Change-driven data flow image processing architecture for optical flow computation

Optical flow computation has been extensively used for motion estimation of objects in image sequences. The results obtained by most optical flow techniques are computationally intensive due to the large amount of data involved. A new change-based data flow pipelined architecture has been developed implementing the Horn and Schunk smoothness constraint; pixels of the image sequence that significantly change, fire the execution of the operations related to the image processing algorithm. This strategy reduces the data and, combined with the custom hardware implemented, it achieves a significant optical flow computation speed-up with no loss of accuracy. This paper presents the bases of the change-driven data flow image processing strategy, as well as the implementation of custom hardware developed using an Altera Stratix PCI development board.

Validating functional equivalence of re-engineered programs via control path,result, and data flow comparison

A combination of three approaches is proposed here to validate the functional equivalence of re-engineered programs. These are: control path comparison; data result comparison; and data flow comparison. Two of the three approaches are based on the theory of program slicing. The third approach is based on the theory of data invariance, i.e. the same input must produce the same output. Together, these techniques can provide a cost effective regression test environment for large scale re-engineering projects.     

A dynamic balanced flow for filtering point-sampled geometry

3D point data acquisition has become a practical approach for generating complex 3D shapes. Subsequent smoothing or denoising operations on these raw data sets are required before performing sophisticated modeling operations. Based on covariance analysis and constructed directional curvature, a new approach of anisotropic curvature flow is developed for filtering the point data set. By introducing a forcing term, a balanced flow equation is constructed, which allows the anisotropic diffusion flow to be restricted in the flow diffusion band of the original surface. Thus, the common problem of shape shrinkage that puzzles most current denoising approaches for point-sampled geometry is avoided. Applying dynamic balance techniques, the equation converges to the solution quickly with appealing physical interpretations. The algorithms operate directly on the discrete sample points, requiring no vertex connectivity information. They are shown to be computationally efficient, robust and simple to implement.     

Experiences with a parallel algorithm for data flow analysis

We have designed a family of parallel data flow analysis algorithms for execution on distributed-memory MIMD machines, based on general-purpose, hybrid algorithms for data flow analysis [Marlowe and Ryder 1990]. We exploit a natural partitioning of the hybrid algorithms and explore a static mapping, dynamic scheduling strategy. Alternative mapping-scheduling choices and refinements of the flow graph condensation used are discussed. Our parallel hybrid algorithm family is illustrated on Reaching Definitions, although parallel algorithms also exist for many interprocedural (e.g., Aliasing) and intraprocedural (e.g., Available Expressions) problems [Marlowe 1989]. We have implemented the parallel hybrid algorithm for Reaching Definitions on an Intel iPSC/2. Our empirical results suggest the practicality of parallel hybrid algorithms.An earlier version of this paper was presented at Supercomputing '90.The research reported here was supported, in part, by the New Jersey Commission on Science and Technology and the CAIP Center's Industrial Members, by Siemens Research Corporation and by National Science Foundation grant CCR-8920078.     

基于Hadoop的云端异常流量检测与分析平台

Hadoop系统作为一种开源的分布式云计算平台已获得广泛应用,但其云端易受到各种威胁和攻击,基于此,开发了一种基于Hadoop的云端异常流量检查与分析平台。首先,使用Mapper周期性地从所有存储流量信息的文件中提取流量的部分信息;然后,通过Reducer将异常流量提取并保存。通过对流量数据的存储、检测与分析可成功地检测出有威胁的攻击,从而保障云端的安全。由于本平台基于开源的Hadoop实现,因此成本较低;同时,基于Java语言实现,可成功移植于各种主流操作系统,具有广泛适用性。基于局域网进行监控试验,结果表明本平台可成功地检测出异常流量,并输出友好的用户界面。     

Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling

Modern infrastructure increasingly depends on large computerized systems for their reliable operation. Supervisory Control and Data Acquisition (SCADA) systems are being deployed to monitor and control large scale distributed infrastructures (e.g. power plants, water distribution systems). A recent trend is to incorporate Wireless Sensor Networks (WSNs) to sense and gather data. However, due to the broadcast nature of the network and inherent limitations in the sensor nodes themselves, they are vulnerable to different types of security attacks. Given the critical aspects of the underlying infrastructure it is an extremely important research challenge to provide effective methods to detect malicious activities on these networks. This paper proposes a robust and scalable mechanism that aims to detect malicious anomalies accurately and efficiently using distributed in-network processing in a hierarchical framework. Unsupervised data partitioning is performed distributively adapting fuzzy c-means clustering in an incremental model. Non-parametric and non-probabilistic anomaly detection is performed through fuzzy membership evaluations and thresholds on observed inter-cluster distances. Robust thresholds are determined adaptively using second order statistical knowledge at each evaluation stage. Extensive experiments were performed and the results demonstrate that the proposed framework achieves high detection accuracy compared to existing data clustering approaches with more than 96% less communication overheads opposed to a centralized approach.