A static analyzer for finding dynamic programming errors

There are important classes of programming errors that are hard to diagnose, both manually and automatically, because they involve a program's dynamic behavior. This article describes a compile‐time analyzer that detects these dynamic errors in large, real‐world programs. The analyzer traces execution paths through the source code, modeling memory and reporting inconsistencies. In addition to avoiding false paths through the program, this approach provides valuable contextual information to the programmer who needs to understand and repair the defects. Automatically‐created models, abstracting the behavior of individual functions, allow inter‐procedural defects to be detected efficiently. A product built on these techniques has been used effectively on several large commercial programs. Copyright © 2000 John Wiley & Sons, Ltd.     

Functional programming analysis

An analysis of several routines from a large real time software system, using recently developed functional programming theory, has shown that the functional capabilities of the routines can be constructed from analysis of the code text. This analysis also showed that the number of distinct functions computed by a program is much smaller than generally appreciated. Many apparent logic paths are not executable, some executable logic paths compute the same function (on different input subsets), and some functions are unnecessarily fragmented by excessive logic tests. With the aid of information derived from the analysis, the routines were restructured into simpler forms, having fewer executable statements and a more visible relationship of code text to functional capabilities. Some of the restructured routines have higher performance—shorter execution time and less primary storage usage. The application of functional programming to the generation of test cases to demonstrate satisfaction of functional requirements, software maintenance, and construction of new programs having visible correspondence to functional requirements is also discussed.     

Logic programming and digital circuit analysis

We show how the unique character of logic programming can be exploited for the purpose of specifying and automatically reasoning about electrical circuits. Although propositional logic has long been used for describing the truth functions of combinational circuits, the more powerful predicate calculus on which logic programming is based has seen relatively little use in design automation. Previous researchers have introduced a number of techniques similar to logic programming, but many of the useful consequences of the methodology have not been demonstrated. We describe particular consequences of using this method for writing directly executable specifications of circuits, including the use of quantified variables, verification of hypothetical states, and sequential simulation. We have used these methods to solve problems in gate assignment, specialization of standard definitions, and determination of signal flow.     

Collection and analysis of microprocessor design errors

Research on practical design verification techniques has long been impeded by the lack of published, detailed error data. We have systematically collected design error data over the last few years from a number of academic microprocessor design projects. We analyzed this data and report on the lessons learned in the collection effort     

Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems

In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.     

对非线性规划问题的并行求解与分析

根据非线性规划问题的网格并行运算的要求,在分析非线性最小二乘问题的迭代法的基础上,提出了非线性最小二乘问题的并行迭代法来提高其并行度.然后给出检验函数,使用C MPI语言编写非线性最小二乘问题的并行程序,再将其转换为RSL作业脚本,实时地监控其状态信息,并对返回信息做出分析,证明了并行迭代法的优越性.     

Linear and quadratic programming neural network analysis

Neural networks for linear and quadratic programming are analyzed. The network proposed by M.P. Kennedy and L.O. Chua (IEEE Trans. Circuits Syst., vol.35, pp.554-562, May 1988) is justified from the viewpoint of optimization theory and the technique is extended to solve optimization problems, such as the least-squares problem. For quadratic programming, the network converges either to an equilibrium or to an exact solution, depending on whether the problem has constraints or not. The results also suggest an analytical approach to solve the linear system Bx =b without calculating the matrix inverse. The results are directly applicable to optimization problems with C(2) convex objective functions and linear constraints. The dynamics and applicability of the networks are demonstrated by simulation. The distance between the equilibria of the networks and the problem solutions can be controlled by the appropriate choice of a network parameter.     

语音识别错误的分类分析

大词表连续语音识别系统由多个组件构成,识别错误受多种因素的影响。系统开发者需要分析错误发生的不同原因。根据语音识别的基本理论给出了对错误进行分类分析的原理,将识别错误按错误原因分为解码错误、声学模型错误、语言模型错误、声学和语言复合错误四大类,并对分类后的错误做了统计分析。实验证明,识别错误的分类分析为系统的改进提供了参考依据。     

A statistical analysis of syntax errors

A study of errors made by Pascal programmers is described. The results of this study are discussed in relation to compiler syntax error recovery procedures. It is found that syntax errors made in practice are quite simple and occur relatively infrequently (generally at most one per sentence of the language). Also a few types of errors account for most occurrences. These and other findings from the study are helpful in evaluating compiler error handling procedures. A discussion of the relation between these errors and language constructs is presented. Other uses of the information obtained from the study are briefly described.     

Solution errors in finite element analysis

The development of the finite element method so far indicates that it is a discretization technique especially suited for positive definite, self-adjoint, elliptic systems, or systems with such components. The application of the method leads to the discretized equations in the form of $\text{u}\text{?}\text{=}\text{f(u)}$, where u lists the response of the discretized system at n preselected points called nodes. Instead of explicit expressions, vector function f and its Jacobian f,_u are available only numerically for a numerically given u. The solution of $\text{u}\text{?}\text{=}\text{f(u)}$ is usually a digital computer. Due to finiteness of the computer wordlength, the numerical solution $\text{u}{}_{\mathrm{c}}$ is in general different from u. Let $\text{u}\text{(}\text{x}\text{, t)}$ denote the actual response of the system in continuum at points corresponding to those of u. In the literature. $\text{u}\text{(}\text{x}\text{, t)-}\text{u}$ is called the discretization errors, $\text{u}\text{-}\text{u}{}_{\mathrm{c}}$ the round-off errors, and the s is. $\text{u}\text{(}\text{x}\text{, t)-}\text{u}{}_{\mathrm{c}}$ is called the solution errors. In this paper, a state-of-the-art survey is given on the identification, growth, relative magnitudes, estimation, and control of the components of the solution errors.     

Local stability analysis using simulations and sum-of-squares programming

The problem of computing bounds on the region-of-attraction for systems with polynomial vector fields is considered. Invariant subsets of the region-of-attraction are characterized as sublevel sets of Lyapunov functions. Finite-dimensional polynomial parametrizations for Lyapunov functions are used. A methodology utilizing information from simulations to generate Lyapunov function candidates satisfying necessary conditions for bilinear constraints is proposed. The suitability of Lyapunov function candidates is assessed solving linear sum-of-squares optimization problems. Qualified candidates are used to compute invariant subsets of the region-of-attraction and to initialize various bilinear search strategies for further optimization. We illustrate the method on small examples from the literature and several control oriented systems.