William M. Lively

A graphical specification system for user-interface design

An environment for creating user interfaces for embedded systems, called the graphical specification system (GSS), is presented. GSS combines graphical and minimal low-level textual specification with a prototyping capability for rapid user-interface design and evaluation. It is part of a larger embedded systems project at Lockheed, called Express. The user interface components, display components, user-machine interaction, interface-application interaction, and executive component are discussed. Two scenarios, developed with GSS tool prototypes, demonstrate how some GSS tools function. One is the construction of a display with two pairs of gauges, one Cartesian and one polar. The other is the design of a display for submarine tracking.<<ETX>>

Graphical specification of user interfaces with behavior abstraction

The Application Display Generator (ADG) is a graphical environment for the design and implementation of embedded system user interfaces. It is a major component of the Graphical Specification Subsystem (GSS) in Lockheeds Express knowledge-based software development environment. ADG gives non-programmers simple and flexible methods for graphically specifying the presentation and behavior of embedded system user interfaces. In the ADG methodology arbitrary presentations are attached to abstract object behaviors. This approach makes it possible to provide unconstrained presentations, intelligent user support, rapid prototyping, and flexible facilities for composing complex objects.

Using a neural network to predict student responses

One ,,f the ilnportant components in an intelligent tut(,ring systenl is the student model. This model is used to predict what t}lr s! udent may do next as well as to serve as a repository of past student solutions. The student model is important in that it <an help to direct the student to unknown material when rmough ~,,ncepts have been mastered and to materiaf that needs to be IX:,Viewwf whep the student is ~sure. Some stud.en~ models. have tried t.u predict student solution steps by restricting the interface to the point where the student cannot make an unknown move. others do not concentrate on prediction, but instead concentrate on remedying errors in problem solutions. Since the problem of prediction is difficult, another tool, the neural network, shodd pruve useful. Neural networks have the ability to generalize over a set of student answers. This ability gives the network the capacity to answer as the student would on problems that the network has never seen before. Given this exciting p{,ssibility, research has been started using the bacfrpropagation model of neural networks to learn a student’s method in perf{,rming subtraction. The preliminary results reported in this r,aper are encouraging and serve to show the promise of neural nrt works in the student model of intelligent tutoring systems.

Automatic development of parallel simulation models in ADA

This paper introduces MultiSim, a prototype, user-oriented tool specifically designed to automate the model development process for parallel simulation models. Targeted toward the simulationist and written in Ada for high transportability among different numbers of processors, MultiSim combines discrete-event simulation knowledge, parallel programming knowledge, and target language knowledge and represents this knowledge in frame-like constructs. Through user interaction, knowledge of the system to be modeled is abstracted and a parallel Ada simulation model is automatically generated based on the knowledge resident within MultiSim.

Web software traffic characteristics and failure prediction model selection

In this paper, we analyze web traffic characters and the relationship with software failures. Results indicate hourly web access traffic is the lowest from 3:00 to 4:00 am, while the traffic load gradually reaches peak between 14:00 and 16:00, before declining. In daily base, web traffic fluctuates in the 25 observed days. The hourly access hits appear in similar patterns to the software failures. The web site reliability is 0.9878. The mean time between failures is 82.03 hits. Five popular software reliability models are calibrated with real data. The validations show that Goel-Okumoto and Gompertz models accurately describe web software failures. Further investigations indicate that both models have some deviations in prediction accuracy starting from the 20th day. Using similar approach to change-point solutions, we recalibrate the models with different parameter values after 20th day. The results appear that two sets of parameter values greatly improve model prediction accuracy.

Software security analysis and assessment model for the web-based applications

For web-based applications, a security analysis was conducted in order to identify software vulnerabilities and develop a new security assessment model. During such an analysis, the major security vulnerabilities observed in the open web proxy honeypot during the data collection time from February to March 2005 were computer worm attacks in Code Red and Nimda, AWSTAT attacks, unauthorized access request (HTTP error code 403), MS-SQL version overflow attacks, etc. To develop the security assessment model, we extended the generic security model for a single component system to multiple components using the multidimensional Markov process model. The resulting model was applied to the most popular software systems. The software system availability in security is computed using real data, and the mean time to security failure is calculated. This paper not only provides the software vulnerability analysis of the web-based applications, but also details the software security assessment for multiple component systems.

The development of an integrated mathematical and knowledge-based maintenance delivery system

Abstract In this paper, we investigate a maintenance problem from a continuous manufacturing environment. The specific maintenance policies to be considered are whether to have replacement, minimal repair or no action. The objective function is based on discounted costs and will be structured so that changes in manufacturing conditions can be easily incorporated into the decision-making process. The delivery tool for the mathematical model will be a system integrating a rule-based expert system with an optimization procedure involving the mathematical analysis of replacement models.

Object-Oriented Software Development Effort Prediction Using Design Patterns from Object Interaction Analysis

Software project management is arguably the most important activity in modern software development projects. In the absence of realistic and objective management, the software development process cannot be managed in an effective way. The authors propose a holistic approach, SysML Point Model, which is based on a common, structured and comprehensive systems engineering modeling language (OMG SysML). Critical to the SysML Point estimation is the Pattern Point Model, a Function Point-like methodology, that produces an estimate of the size of OO (Object-Oriented) development projects using the design patterns found in object interaction modeling from the late OO analysis phase. Two measures are defined (PP1 and PP2) and an initial empirical validation is performed to assess the usefulness and effectiveness of the measures in predicting the development effort of object-oriented systems. The experimental results show that the Pattern Point measure can be effectively used during the OO analysis phase to predict the effort values with a high degree of confidence. The PP2 metric yielded the best results with an aggregate PRED (0.25) = 0.874.

Principled modeling and automatic classification for enhancing the reusability of problem-solving methods of expert systems

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.

Principled modeling and automatic classification of functional requirements for improved reusability of the design of knowledge-based systems

One of the major issues in modeling expert systems (ESs) for enhanced reusability is capturing a high-level view of their operations. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques to explicitly capture the functional requirements of ESs has not been fully explored. This paper investigates issues and provides solutions to the use of an AI knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. Its benefits are demonstrated using two real world knowledge-based systems.<<ETX>>