基于数据分解的并发面向对象程序开发方法

提出了一种从VDM-SL(Vienna development method-specification language)规约到并发面向对象程序的开发方法,这种方法基于DD-VDM(data decomposition-Vienna development method).在此基础上提出了虚拟原子、服务并行和内部并行等概念,继而提出一种嵌套面向对象结构来体现这些功能.分别从共享量并行系统和分布并行系统的角度讨论了嵌套面向对象结构的实现技术.     

Informal and formal requirements specification languages: bridgingthe gap

The differences between informal and formal requirements specification languages are noted, and the issue of bridging the gap between them is discussed. Using structured analysis (SA) and the Vienna development method (VDM) as surrogates for informal and formal languages, respectively, two approaches are presented for integrating the two. The first approach uses the SA model of a system to guide the analyst's understanding of the system and the development of the VDM specifications. The second approach proposes a rule-based method for generating VDM specifications from a set of corresponding SA specifications. The two approaches are illustrated through a simplified payroll system case. The issues that emerge from the use of the two approaches are reported     

Validating voice communication requirements using lightweightformal methods

To show that lightweight approaches can facilitate the technological transfer of formal development methods, the authors report on their experience using VDM++ to specify a safety-critical air traffic control voice communication system. Their approach raised both the quality of the informal system specification and the efficiency of the system test suites they used     

A safe regression testing approach for safety critical systems

Regression testing is important activity during the software maintenance to deal with adverse effects of changes. Our approach is important for safety critical system as usually formal methods are preferred and highly recommended for the safety critical systems but they are also applied for the systems development of other than critical system. Our approach is based on Regression testing using VDM++ which takes two VDM++ specifications, one baseline and other delta (Changed) along with test suite for the baseline version. It compares both versions by using comparator module, identifies the change. By analyzing the change we classify the test cases from original test suite into obsolete, re-testable, and reusable test cases. Our scope is at unit level i.e. at class level. Our approach gets two versions of VDM++ specification and returns regression test suite for the delta version. Our approach distinguishes test cases which are still effective for the delta version of VDM++ specification and it differs from re-test all strategy as it can distinguish the test cases and identifies test cases which are useful for delta version. Test cases reusability and test case reduction is the main objective of our approach. Our approach presents how to perform regression testing using VDM++ specification during the maintenance of systems.     

Developing ada programs using the vienna development method (VDM)

A method is presented for the systematic development of sequential Ada programs using the Vienna Development Method (VDM). The approach is based upon using the facilities of Ada for supporting parameterized abstract data types to implement the primitives of the VDM specification language. More experimental work is required to assess the utility of the method, but a systematic approach is promised for using features of Ada unfamiliar to most programmers (e.g. packages, generics and exceptions). Familiarity with Ada is assumed. An overview of VDM is presented so that unfamiliar readers can understand the example given in the paper.     

A System for Translating Executable VDM Specifications into Lazy ML

This article describes the process of developing a system for translating VDM executable specifications into Lazy ML prototypes. The system was specified in VDM and a Lazy ML prototype implementation was derived from its specification. This article concentrates on discussing the lessons learned in each stage of the development process, evaluating the adequacy of the used methodology. © 1997 by John Wiley & Sons, Ltd.     

An alternate method for using a visual discrimination model (VDM) to optimize soft-copy display image quality

Researchers have developed visual discrimination models (VDMs) that can predict a human observer's ability to detect a target object superposed on an image. These models incorporate sophisticated knowledge of the properties of the human visual system. In the predictive approach, termed conventional VDM usage, two input images with and without a target are analyzed by an algorithm that calculates a just-noticeable-difference (JND) index, which is a taken as a measure of the detectability of the target. A new method of using the VDM is described, termed channelized VDM, which involves finding the linear combination of the VDM-generated channels (which are not used in conventional VDM analysis) that has optimal classification ability between normal and abnormal images. The classification ability can be measured using receiver operating characteristic (ROC) or two alternative forced choice (2AFC) experiments, and in special cases they can also be predicted by signal detection theory (SDT) based model-observer methods. In this study simulated background and nodule containing regions were used to validate the new method. It was found that the channelized VDM predictions were in excellent qualitative agreement with human-observer validated SDT predictions. Either VDM method (conventional or channelized) has potential applicability to soft-copy display optimization. An advantage of any VDM-based approach is that complex effects, such as visual masking, are automatically accounted for, which effects are usually not included in SDT-based methods.     

An abstract programming language and correctness proofs

The realization of an abstract programming language is a good approach for automating the software production process and facilitating the correctness proof of a software system.

This paper introduces a formal language for programming at the abstract level by combining Pascal with VDM (Vienna Development Method). The notation provided by the language obliges programmers to consider the correctness of programs throughout the whole process of programming, and the proof axiom and rules presented in this paper may be used to prove the correctness of programs. A complete example is given to illustrate how to program using APL and how to prove the correctness of programs using the given axiom and rules.     

An approach to literate and structured formal developments

We present an approach to the literate and structured presentation of formal developments. We discuss the presentation of formal developments in a logical framework and distinguish three aspects: language-related aspects, structural aspects of proofs, and presentational aspects. We illustrate the approach by two examples: a simple mathematical proof of the Knaster-Tarski fixpoint theorem, and a formalization of the VDM development of a revision management system.     

Unification: A case-study in data refinement

In this paper, the Z notation is used to develop a small theory of terms and substitutions within which a simple unification algorithm can be specified and proved correct. Particular emphasis is placed on the use of Z's mathematical data types to simplify the development and structure of this theory. The correctness of an abstract version of the algorithm is proved first; this version operates on substitutions by composition. Then data refinement is used to show that the substitutions can be represented by binding functions that make composition a particularly efficient operation. The approach taken in this paper is compared with the approaches of three previous papers based on VDM.The contribution of this paper is to show how data refinement can be used to explain the design decisions behind a non-trivial program, and to provide a point of comparison between Z and VDM approaches to the same problem.     

A Formal Semantics of Data Flow Diagrams

This paper presents a formal semantics of data flow diagrams as used in Structured Analysis, based on an abstract model for data flow transformations. The semantics consists of a collection of VDM functions, transforming an abstract syntax representation of a data flow diagram into an abstract syntax representation of a VDM specification. Since this transformation is executable, it becomes possible to provide a software analyst/designer with two ‘views’ of the system being modelled: a graphical view in terms of a data flow diagram, and a textual view in terms of a VDM specification. In this paper emphasis is on the motivation for the choices made in the transformation. The main aspects of the transformation itself are described using annotated VDM functions with some examples.     

Methods for the Development of Distributed Real-Time Embedded Systems Using VDM

The development of distributed real-time embedded systems presents a signi-ffcant practical challenge both because of the complexity of distributed computation and because of the need to rapidly assess a wide variety of design alternatives in early stages when requirements are often volatile. Formal methods can address some of these challenges but are often thought to require greater initial investment and longer development cycles than is desirable for the development of noncritical systems in highly competitive markets.In this paper we propose an approach that takes advantage of formal modelling and analysis technology in a lightweight way, making signi cant use of readily available tools. We describe an incremental approach in which detail is progressively added to abstract system-level speci cations of functional and timing properties via intermediate models that express system architecture, concurrency and distribution. The approach is illustrated using a modelof a home automation system. The models are expressed using the Vienna Development Method (VDM) and are validated primarily by scenario-based tests.     

Toward value difference metric with attribute weighting

In distance metric learning, recent work has shown that value difference metric (VDM) with a strong attribute independence assumption outperforms other existing distance metrics. However, an open question is whether VDM with a less restrictive assumption can perform even better. Many approaches have been proposed to improve VDM by weakening the assumption. In this paper, we make a comprehensive survey on the existing improved approaches and then propose a new approach to improve VDM by attribute weighting. We name the proposed new distance function as attribute-weighted value difference metric (AWVDM). Moreover, we propose a modified attribute-weighted value difference metric (MAWVDM) by incorporating the learned attribute weights into the conditional probability estimates of AWVDM. AWVDM and MAWVDM significantly outperform VDM and inherit the computational simplicity of VDM simultaneously. Experimental results on a large number of UCI data sets validate the performance of AWVDM and MAWVDM.     

虚拟数字机系统的设计及实现

虚拟数字机是仿真实计算机的一种系统软件，该文介绍了虚拟数字机系统的设计思想、框架结构和关键技术，给出了基于虚拟数字机应用的实例，通过实验验证了该方法的有效性。     

The virtual distortion method—a versatile reanalysis tool for structures and systems

For 20 years of development, the virtual distortion method (VDM) has proved to be a versatile reanalysis tool in various applications, including structures and truss-like systems. This article presents a summary of principal achievements, demonstrating the capabilities of the VDM both in statics and dynamics, in linear and nonlinear analysis. The major advantage of VDM is its exactness and no need for matrix inversion in the reanalysis algorithm. The influence matrix—numerical core of the VDM—contains the whole mechanical knowledge about a structure, by looking at all global responses due to local disturbances. The strength of the method is demonstrated for truss structures.     

Developing parallel object-oriented programs in the framework of VDM

After surveying the rely-guarantee and some related approaches to extending VDM to develop parallel programs, two main problems are found. One problem is that all explorations of parallelism are done in the stage of operation decomposition or afterwards so that the degree of parallelism is restricted. Another problem is that the atomicity is fixed at one level and the development complexity can not be controlled effectively because there is no natural means to let the level of granularity be under flexible control of the designer. In order to solve these two problems, we introduce a new concept — data decomposition which is based on the ideas of model split, modularisation and operation decomposition, and combine it with VDM to form a more general formal development method DD-VDM, in which some kind of operation decompositions, i.e., operation split can be done before some data reifications. Then a nested parallel object-oriented structure is proposed. Combining these ideas into the unified framework, this paper presents a hierarchical object-oriented design methodology in which two kinds of parallelism, that is, internal parallelism and service parallelism, can be exploited gradually and a kind of virtual atomicity is provided.This research is partially supported by China's National Foundation for Excellent Young Scientists.     

Dissimilarity learning for nominal data

Defining a good distance (dissimilarity) measure between patterns is of crucial importance in many classification and clustering algorithms. While a lot of work has been performed on continuous attributes, nominal attributes are more difficult to handle. A popular approach is to use the value difference metric (VDM) to define a real-valued distance measure on nominal values. However, VDM treats the attributes separately and ignores any possible interactions among attributes. In this paper, we propose the use of adaptive dissimilarity matrices for measuring the dissimilarities between nominal values. These matrices are learned via optimizing an error function on the training samples. Experimental results show that this approach leads to better classification performance. Moreover, it also allows easier interpretation of (dis)similarity between different nominal values.