A Constructing Method of Functional Model by Integrated Learning from Examples of Software Modification
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E78-D No.9 pp.1133-1141
- Publication Date
- 1995/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Knowledge Based Software Engineering)
- Category
- Models
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Hiroyuki YAMADA, Tetsuo KOBASHI, Tsunehiro AIBARA, "A Constructing Method of Functional Model by Integrated Learning from Examples of Software Modification" in IEICE TRANSACTIONS on Information,
vol. E78-D, no. 9, pp. 1133-1141, September 1995, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e78-d_9_1133/_p
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@ARTICLE{e78-d_9_1133,
author={Hiroyuki YAMADA, Tetsuo KOBASHI, Tsunehiro AIBARA, },
journal={IEICE TRANSACTIONS on Information},
title={A Constructing Method of Functional Model by Integrated Learning from Examples of Software Modification},
year={1995},
volume={E78-D},
number={9},
pages={1133-1141},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - A Constructing Method of Functional Model by Integrated Learning from Examples of Software Modification
T2 - IEICE TRANSACTIONS on Information
SP - 1133
EP - 1141
AU - Hiroyuki YAMADA
AU - Tetsuo KOBASHI
AU - Tsunehiro AIBARA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E78-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 1995
AB - 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.
ER -
English
