To clarify the effect of collaborative filtering (CF) on recommending highgenerality / low-generality software components, we experimentally verified two hypotheses; (1) the recommendation accuracy of CF for high-generality components is better than that of conventional methods (random algorithm and user average algorithm) and (2) the recommendation accuracy of CF for lowgenerality components is better than that of the conventional methods. We evaluated recommendation accuracy of CF with a dataset containing 29 open source software development projects (including 2,558 used components). As ...