One approach to develop software efficiently is to reuse existing software by modifying a part of it. However, modifying software will often introduce unexpected side effects into other parts of it. As a result, it costs much time and care to modify the software. So, in order to modify software efficiently, we have proposed a functional model to represent information about side effects caused by modification and a model based supporting system for modifying software. So far, however, an expert software developer must describe the entire functional model of the target software through the analysis of practical modifying processes. This will be an unnecessary burden on him. Moreover, the larger target software becomes, the harder the model construction becomes. Therefore, an automatic constructing method of the functional model is needed in order to solve this problem. So, this paper considers a method of acquiring useful interaction information by learning from training examples of modification. However, in our application domain, it seems that it is impossible to make complete domain theory and to prepare a large number or training examples in advance. Therefore, our learning method involves an integration of explanation-based learning (EBL) from positive examples of modification generated by the user and Similarity-based learning (SBL) from positive or negative examples generated by the user and the learning system. As a result, our method can acquire valid knowledge about the interaction from not so many examples under incomplete theory. Then, this paper presents a constructing method, in which our proposed learning method is incorporated, of a functional model. Finally, this paper demonstrates construction of the functional model in the domain of an event-driven queueing simulation program according to our learning method.

This paper deals with the fundamental problem of automatic assessment of appearance of seam puckers on suits, and suggests possibilities for practical usage. Presently, evaluations are done by inspectors who compare standard photographs of suits to test samples. In order to avoid human errors, however, a method of automatic evaluation is desired. We process the problem as pattern recognition. As a feature we use fractal dimensions. The fractal dimensions obtained from standard photographs are used as template patterns. To make it easier to calculate fractal dimensions, we plot a curve representing the appearance of seam puckers, from which fractal dimensions of the curve can be calculated. The seam puckers in gray-scale images are confused with the material's texture, so the seam puckers must be enhanced for a precise evaluation. By using the concept of variance, we select images with seam puckers and enhance only the images with seam puckers. This is the novel aspect of this work. Twenty suits are used for the evaluation experiment and we obtain a result almost the same to the evaluation gained by inspection. That is, the evaluation of 11 samples is the same as that gained by inspection, the results of 8 samples differ by 1 grade, and the evaluation of 1 sample has a 2-grade difference. The results are also compared to the evaluation of the system using the Daubechies wavelet feature. The result of comparison shows that the present method gives a better evaluation than the system using the Daubechies wavelet.