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 dif ferent 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 dif ferent 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 dif ferent types of errors have a statistically significant correlations with each other . Further , the existence of dif ferent errors types are statistically related . As a result , errors signal the existence of other errors . ÷ 1996 Academic Press Limited