A science is most exciting when there are two or more strong, competing theories. When a new, major theory replaces an older one, one speaks of a paradigm shift, while the stable periods in between are called ``normal science''. Physics provides interesting examples of paradigm shifts.
There are a few competing theories in computer science, none of them earth-shaking. The physical symbol system theory vs. the knowledge processing theory in AI is one of them. These two theories attempt to explain intelligence. The weak reasoning methods of the first theory have gradually given way, or have been coupled with, knowledge bases [4]. Other examples include symbolic vs. subsymbolic processing, RISC vs. CISC, the various models for predicting the performance of (parallel) computers, and the competition among programming language families (logic, functional, imperative, object-oriented, rule-based, constraint-based). Another important example is algorithms theory. The present theory has many drawbacks; in particular, it does not account for the behavior of algorithms on ``typical'' problems[7]. A more accurate theory that applies to modern computers would be valuable.
A prerequisite for competition among theories is falsifiability.
Unfortunately, computer science theorists rarely produce
falsifiable theories; they tend to pursue mathematical theories that
are disconnected from the real world.
Thus,
it has largely fallen to experimentalists and engineers to formulate
falsifiable theories.
While computer science is perhaps too young to have brought forth grand theories, my greatest fear is that the lack of such theories might be caused by a lack of experimentation. If scientists neglect experiment and observation, they'll have difficulties discovering new and interesting phenomena worthy of better theories.