資訊系統乃企業組織於資訊時代增加競爭力之利器。故資訊軟體將不斷的被規劃及開發，但軟體開發過程中如果沒有良好的規劃管理，容易發生開發時程延長、品質不佳、人力資源分配不適當等問題，影響軟體後續管理及維護程序，而準確的軟體成本預估是專案預算、時程、人員、設備資源等規劃分配的依據，如果憑藉主觀盲目的制定專案計劃，執行起來和實際情況常會有所出入，導致成本超支、開發時程無法控制，專案失敗機會將會大幅上昇。
傳統上常使用程式碼行數(Lines of Code, LOC)和功能點(Functional Point, FP)的方式來估計軟體成本，已經無法獲得良好的效果。近年來許多學者使用倒傳遞網路(Back Propagation Network, BPN)預估軟體成本都比傳統方法有較佳的準確度，而廣義迴歸類神經網路(General Regression Neural Network, GRNN)較倒傳遞網路相比，更有學習速度快、結果穩定、人為選定的參數少之優點。本研究的目的主要在於使用廣義迴歸類神經網路，並結合基因遺傳演算法(Genetic Algorithms, GA)求解最佳化網路參數，利用複製、交配、突變等基因遺傳運算，找出最適合的平滑參數值，提昇廣義迴歸類神經網路的預測能力，進而建構出一個良好的軟體成本估計模式，輔助軟體開發工作的進行。
經與相關軟體開發成本預估文獻比較中証明，本研究所提出之預測模式的準確度較二元樹演算法(Classification and Regression Trees, CART)、類神經網路(Artificial Neural Network, ANN)、普通最小平方迴歸(Ordinary Least Squares Regression, OLS)、類比式模式(Analogy-Based Model)、調整型類比式模式(Adjusted Analogy-Based Model)為佳。

Information Systems can increase company’s competitive strength in the information age. Therefore, information software will keep planning and developing. However, if the software development process without appropriate management, that will prone to extend development time, low quality, improper human resources assignment. This will affect software management and maintenance procedures. So, accurate software cost estimates is the base of budget, time, human resources, and equipment allocation.
Traditionally, Lines of Code (LOC) and Function Points (FP) are usually applied to estimate software cost. But these two methods have been unable to obtain good results. During recent years, many studies use Back Propagation Network to estimate software cost and all the studies are better than the traditional method of the accuracy of software cost prediction. Compared with the General Regression Neural Network (GRNN) and Back Propagation Network (BPN), GRNN have advantages included learning speed fast, stable results, and less parameters. The purpose of this study is to use Genetic Algorithms (GA) integrated GRNN. This research tries to find the most suitable smoothing parameter values of the GRNN, and enhance the ability of forecasting.
By Comparison with software development costs estimated method in related literature, the accuracy of forecasting model in this research is better than Classification and Regression Trees (CART), Artificial Neural Network (ANN), Ordinary Least Squares Regression (OLS), Analogy-Based Model, and Adjusted Analogy-Based Model.

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