There is still much problem in sizing software despite the existence of well-known software sizing methods such as Function Point method. Many developers still continue to use ad-hoc methods or so called "expert" approaches. This is mainly due to the fact that the existing methods require much information that is difficult to identify or estimate in the early stage of a software project. The accuracy of ad-hoc and "expert" methods also has much problem. The entity-relationship (ER) model is widely used in conceptual modeling (requirements analysis) for data-intensive systems. The characteristic of a data-intensive system, and therefore the source code of its software, is actually well characterized by the ER diagram that models its data. This paper proposes a method for building software size model from extended ER diagram through the use of regression models. We have collected some real data from the industry to do a preliminary validation of the proposed method. The result of the validation is very encouraging.

This paper describes techniques of scoring prosodic proficiency of English sentences spoken by Japanese. The multiple regression model predicts the prosodic proficiency using new prosodic measures based on the characteristics of Japanese novice learners of English. Prosodic measures are calculated by comparing prosodic parameters, such as F0, power and duration, of learner's and native speaker's speech. The new measures include the approximation error of the fitting line and the comparison result of prosodic parameters for a limited segment of the word boundary rather than the whole utterance. This paper reveals that the introduction of the new measures improved the correlation by 0.1 between the teachers' and automatic scores.