Three categories of remote computing (client/server) in use and development today are the Network Computer (Java), the Web Browser interface, and Remote Screen/Keyboard. The Remote Screen/Keyboard model offers the greatest flexibility, the 'thinnest clients': and the strongest immunity to the need for upgrades to users' terminals. It also allows the use of lightweight, ultra-portable, wireless terminals while still providing access to the most complex applications (including Internet access, corporate information systems, and E-mail), and to all the files normally available from a user's desktop computer. The terminals control users' Virtual Personal Computers, which reside on servers in the network. The authors have constructed a test system with which users satisfy their computing needs using terminals ranging from one to six pounds, connected by a variety of wireless and wireline means. Performance and usage statistics collected so far, along with coming wireless packet data systems, suggest that the Virtual Personal Computer/Portable Network model is attractive to users and will soon be economically viable.

Studies have shown that random testing can be an effective testing strategy. One of the goals of testing is to estimate the reliability of the program from the test outcomes. The authors extend the Thayer-Lipow-Nelson reliability model (R. Thayer et al., 1978) to account for the cost of errors. They also compare random testing with partition testing by examining upper confidence bounds for the cost weighted performance of the two strategies.>

Random testing is receiving increasing attention in recent years. Aside from its relative simplicity and low cost, studies have shown that random testing is an effective testing strategy. An advantage of random testing is that the reliability of the program can be estimated from the test outcomes. The authors extend the Thayer-Lipow-Nelson reliability model to account for the cost of errors. They also compare random with partition testing by looking at upper confidence bounds for the cost weighted performance of the two strategies.>

Existing expert systems have a high percentage agreement with experts in a particular field in many situations. However, in many ways their overall behavior is not like that of a human expert. These areas include the inability to give flexible, functional explanations of their reasoning processes, and the failure to degrade gracefully when dealing with problems at the periphery of their knowledge. These two important shortcomings can be improved when the right knowledge is available to the system. This paper presents an expert system design, called the integrated diagnostic model (IDM), that integrates two sources of knowledge, a shallow, reasoning-oriented, experiential knowledge base and a deep, functionally oriented, physical knowledge base. To demonstrate the IDM's usefulness in the problem area of diagnosis and repair, an implementation in the mechanical domain is described.

Random testing of programs has usually (but not always) been viewed as a worst case of program testing. Testing strategies that take into account the program structure are generally preferred. Path testing is an often proposed ideal for structural testing. Path testing is treated here as an instance of partition testing, where by partition testing is meant any testing scheme which forces execution of at least one test case from each subset of a partition of the input domain. Simulation results are presented which suggest that random testing may often be more cost effective than partition testing schemes. Also, results of actual random testing experiments are presented which confirm the viability of random testing as a useful validation tool.

Mills capture-recapture sampling method allows the estimation of the number of errors in a program by randomly inserting known errors and then testing the program for both inserted and indigenous errors. This correspondence shows how correct confidence limits and maximum likelihood estimates can be obtained from the test results. Both fixed sample size testing and sequential testing are considered.