A Constructing Method of Functional Model by Integrated Learning from Examples of Software Modification

Hiroyuki YAMADA  Tetsuo KOBASHI  Tsunehiro AIBARA  

IEICE TRANSACTIONS on Information and Systems   Vol.E78-D   No.9   pp.1133-1141
Publication Date: 1995/09/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Knowledge Based Software Engineering)
Category: Models
explanation based learning,  functional model,  integrated learning,  similarity based learning,  software modification support,  

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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.