ISR: a database for symbolic processing in computer vision

ISR (international symbolic representation), a representation and management system for use at the intermediate (symbolic) level of vision, is described. ISR mediates access to intermediate-level vision data and forms an active interface to the higher-level inference processes that construct an image's interpretation. The system supports important types of data and operations and can be adapted to the changing needs of ongoing research. It provides a centralized data representation that supports integration of results from multiple avenues of research into the overall vision system. ISR's underlying computational paradigm is explained, database requirements for image interpretation are identified, the ISR data management system is described, and the use of ISR is discussed     

An evaluation of the effectiveness of symbolic testing

The effectiveness in discovering errors of symbolic evaluation and of testing sad static program analysis are studied. The three techniques are applied to a diverse collection of programs and the results compared. Symbolic evaluation is used to carry out symbolic testing and to generate symbolic systems of path predicates. The use of the predicates for automated test data selection is analysed. Several conventional types of program testing strategies are evaluated. The strategies include branch testing, structured testing and testing on input values having special properties. The static source analysis techniques that are studied include anomaly analysis and interface analysis. Examples are included which describe typical situations in which one technique is reliable but another unreliable. The effectiveness of symbolic testing is compared with testing on actual data and with the use of an integrated methodology that includes both testing and static source analysis. Situations in which symbolic testing is difficult to apply or not effective are discussed. Different ways in which symbolic evaluation can be used for generating test data are described. Those ways for which it is most effective are isolated. The paper concludes with a discussion of the most effective uses to which symbolic evaluation can he put in an integrated system which contains all three of the validation techniques that are studied.     

EFSM模型的字符串类型测试数据自动生成

基于软件描述模型的测试数据自动生成研究中,字符串类型测试数据生成是一个研究热点和难点。EFSM模型是一种重要的软件描述模型。分析了EFSM模型的特点,针对面向EFSM模型目标路径的字符串测试数据生成,建立了字符串输入变量模型和操作模型,结合静态测试的特点,给出了通过字符串变量模型在目标路径上的符号执行结果生成字符串类型测试数据的方法。实验结果表明,该方法能够达到预期效果,提高测试生成效率。     

Pattern-addressable memory

The pattern associative memory (PAM), a custom associative memory chip specialized to handle the syntax and associated pattern-matching rules common to a range of symbolic processing applications, is discussed. The applications of particular interest are those in which runtime information is captured in the form of a database of declarative expressions. The structure and functioning of an array of these chips, which forms an associative coprocessor for a workstation are described. PAM implementation is addressed     

An interactive program for determining tentative gene assignments from immunological data

This paper announces an interactive program that could be useful for workers investigating new immunogenetic systems and theoreticians seeking to resolve persistent puzzling questions about systems already developed. The program seeks symbolic representations of data and its interpretations. By its ability to work with symbolizations in their most general form it is able to reveal symbolic patterns that may correspond directly to otherwise unexpected genetic models.