The relationship between errors and size in knowledge-based systems

Previous researchers in knowledge-based systems verification have concentrated on developing various approaches and computational tools to find errors in knowledge bases. However, unlike software engineering for traditional systems, there has been little investigation of the relationship between errors and system size. In addition, there has been little analysis of the relationship between the occurrence of different types of errors. Thus, this paper investigates the empirical relationships between knowledge-based system size and number of errors, and between the number and existence of different kinds of errors. It is found that, in general, system size is statistically significantly correlated with two of those error types, and with total errors. Further it is found that the size of ''smaller'' systems is not correlated with total number of errors, but the size of ''larger'' systems is correlated with total number of errors. As a result, this evidence indicates that it can be important to use a modular approach in the development of such systems. In addition, it is found that the number of different types of errors have a statistically significant correlations with each other. Further, the existence of different errors types are statistically related. As a result, errors signal the existence of other errors. (C) 1996 Academic Press Limited