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.
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Hee Beng Kuan TAN, Yuan ZHAO, "Sizing Data-Intensive Systems from ER Model" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 4, pp. 1321-1326, April 2006, doi: 10.1093/ietisy/e89-d.4.1321.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.4.1321/_p
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@ARTICLE{e89-d_4_1321,
author={Hee Beng Kuan TAN, Yuan ZHAO, },
journal={IEICE TRANSACTIONS on Information},
title={Sizing Data-Intensive Systems from ER Model},
year={2006},
volume={E89-D},
number={4},
pages={1321-1326},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e89-d.4.1321},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - Sizing Data-Intensive Systems from ER Model
T2 - IEICE TRANSACTIONS on Information
SP - 1321
EP - 1326
AU - Hee Beng Kuan TAN
AU - Yuan ZHAO
PY - 2006
DO - 10.1093/ietisy/e89-d.4.1321
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2006
AB - 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.
ER -