NORTH-HOLUND

Guest Editor’s Corner Special Issue on Software Quality in Knowledge-Based Systems

Robert Plant Department of Computer Information Systems, University of Miami, Coral Gables, Florida

The term quality has been interpreted in a variety of ways, including definitions by researchers such Garvin (1984), who set out his 8 dimensions quality as follows:

l Performance

l Conformance

l Aesthetics

l Features

l Durability

l Perceived quality

l Reliability

l Serviceability

as of

Carpenter and Murine (1984) proposed the fol- lowing 12 quality factors:

l Correctness

l Integrity

l Maintainability

l Portability

l Reliability

l Reusability

l Testability

l Interoperability

l Efficiency

l Useability

l Flexibility

l Intraoperability

These and other sets of factors have been used by managers and system developers in an attempt to monitor, improve, and understand their products’ quality characteristics. To determine this, sets of metrics for each of the factors need to be created, parameters established for their use, and the inter-

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action of the metrics understood. This far from easy task is made more difficult in the area of software development because of the intangible nature of the product. These problems are further exacerbated in the area of knowledge-based systems because of their nonconventional characteristics such as weak specifications, inexact data, and the need to work with uncertain data. To compound these problems, knowledge-based systems are often used in areas of critical safety or inhospital environments where quality equates to vital systems performance. An example of such a system is an expert system NASA uses to assist in reentry navigation of spacecraft, and this knowledge-based system is examined in Mehro- tra and Wild’s paper in this issue. A failure in this type of system could result in a severity 1 failure, which NASA classifies as a failure leading to loss of crew and vehicle.

The aim of this special issue is to present articles that cover state-of-the-art issues addressing the problems in knowledge-based systems development. The article by Bellman and Landauer considers a key issue that affects not only knowledge-based sys- tems but all systems, that of creating a methodology for large heterogeneous software environments in which many differing types of systems, including knowledge-based systems, have to be unified into one system. This raises many interesting topics, in- cluding how to test and maintain these systems. Bellman and Landauer introduce a new technique known as wrapping to address these issued, which allows for the creation of machine-processable de- scriptions for each of the software processes.

The article by Preece addresses an important topic, that of how the capability level of a knowledge-based system development can be evaluated. He intro- duces the concept of an artifact and uses this as a tool in the development life cycle to assess the

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Editor’s Corner

potential level of system quality, on a scale of one to five, that any given methodology will achieve.

Two articles address issues surrounding the most frequently used knowledge-based system representa- tion scheme: rules. Mehrotra and Wild introduce the concept of multiviewpoint clustering analysis and show the positive effects that partitioning has on a rulebase. The aim is to increase the developer’s ability to understand the system (comprehensibility) and increase maintainability and reliability. In the second article addressing rule representations, Mur- rell and Plant present a formal semantics for a production system architecture. This will allow de- velopers to understand the exact semantics of the representation they frequently use but infrequently have a semantics for (formal or otherwise). This is important because the area of knowledge-based sys- tems has been weak in terms of a formal theory, thus limiting its practical value in some areas.

The article by Kandelin and O’Leary shows how validation and verification techniques can be applied to an object-oriented case study. The article illus- trates the crossover of knowledge-based validation

and verification techniques into what is now consid- ered a conventional programming environment, and how conventional system quality solutions can be brought to bear on knowledge-based issues.

We hope that these articles will encourage re- search and interest in the areas of quality, knowl- edge-based systems, validation, and verification, as well as encourage a cross-fertilization of ideas be- tween knowledge-based and other programming communities.

ACKNOWLEDGMENTS

I thank Robert Glass for his efforts in helping produce this special issue and to all the referees for their time and com- ments. Thanks also go to everyone whose informal com- ments inspired and formulated this issue.

REFERENCES

Carpenter, C. L., and Murine, G. E., Measuring Software Product Quality, Qual. hog. 17, 16-26 (1984).

Garvin, D., What Does “Product Quality” Really Mean? Sloan Manag. Rev. 26, 25-4.5 (1984).