• The Journal of Information Systems
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• v.14 no.2
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• pp.173-189
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• 2005

Recently, Korean software industry has enjoyed fast growth, thus phenomenon drew attention to the need for more precise and standardized cost estimation system for software development. However, many obstacles exist in achieving a more precise and universally applicable cost measurement. These obstacles include : (1) the intangible nature of intellectual efforts, (2) lack of cost standards, (3) lack of quantitative yardstick to determine the potential complication, (4) lack of both cost monitoring during development and coordinating between acquisition process and cost estimation. The purpose of this study is to facilitate software development cost estimation considering acquisition process that will meet korean user's needs, and to create better models that will be cost for software development particularly for Korean environment.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.67-84
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• 2015

Software is more diverse and complex and the level of importance for the maintenance of application software to securely operate software is also gradually increasing in proportion. The calculation method for maintenance cost of application software applied in Korea public enterprises is involved in the range of 10 to 15% of development cost, depending on the Software Project Cost Estimation Guide. Moreover, as most software maintenance cost estimation procedures do not take into consideration of the risk factors related of maintenance, it can be seen as a main cause for the occurrence of maintenance related accidents. This study proposes a maintenance cost estimate model that takes into consideration of the risks related to the software maintenance activities to improve and resolve issues arising from the estimation of maintenance cost. In doing so, maintenance risk factors are analyzed and a risk index is derived through the analysis of risk levels based on the risk factors. Based on such analysis, a maintenance cost estimate method which reflects the maintenance risk index was established.

• Journal of Information Technology Services
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• v.8 no.4
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• pp.129-140
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• 2009

The accurate estimation of software reliability is important to a successful development in software engineering. Until recent days, the models using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software reliability using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying immune algorithm, changing the number of generations, memory cells, and allele. The proposed IA-SVR model outperforms some recent results reported in the literature.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.95-103
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• 2010

Improving the competitiveness of software products in the market involves procuring the means to design software architecture that deliver qualities necessitated by stakeholder requirements within allocated budget, thereby improving the cost-effectiveness of the end product. Currently, software architecture evaluation methods are used to predict and review qualities inherent in software architecture designs and to choose a candidate architecture that delivers desired qualities. Existing software architecture evaluation methods, however, fail to address the cost considerations dependent on the architecture chosen for product implementation. In this paper we suggest a cost estimation method for software architecture which adapts the cost drivers in the software cost estimation model COCOMO II to support cost estimation during architecture evaluation. The suggested method can be performed in coordination with existing software architecture evaluation efforts and supplements existing architecture evaluation techniques with guidelines for identifying and evaluating cost drivers in candidate software architectures without incurring extra overhead. The accuracy of the cost estimation using the suggested method is verified through application of the method to the architecture candidates used in RPS (Robot Patrol System), a surveillance embedded system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.109-110
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• 2015

Estimation of rebar quantity may be used as an index to evaluate the economic feasibility of structural designs. However, when using the software to estimate the rebar quantity, there may be some limitations such as data loss caused by human errors and estimation delays caused by increased input time, since the information on arrangement of rebar is inserted manually. To solve the problems of such quantity estimation software, it is necessary to develop a method on automatic input/output of structural design information for quantity estimation and an algorithm for accurate estimation of rebar quantity. The purpose of this study is to improve the existing rebar quantity estimation by connecting with the database on information related to rebar estimation and the algorithm for rebar estimation, in order to develop an algorithm to estimate an accurate, net rebar quantity. The study result can be used as basic data for development of software for efficient structural designs and automatic framework estimation of buildings.

• Korean Management Science Review
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• v.23 no.2
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• pp.75-91
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• 2006

The purpose of this paper is to propose a new software development cost estimation method using SVR(Support Vector Regression) SVR, one of machine learning techniques, has been attracting much attention for its theoretic clearness and food performance over other machine learning techniques. This paper may be the first study in which SVR is applied to the field of software cost estimation. To derive the new method, we analyze historical cost data including both well-known overseas and domestic software projects, and define cost drivers affecting software cost. Then, the SVR model is trained using the historical data and its estimation accuracy is compared with that of the linear regression model. Experimental results show that the SVR model produces more accurate prediction than the linear regression model.

• Journal of Ocean Engineering and Technology
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• v.19 no.6 s.67
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• pp.50-57
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• 2005

Ice resistance on ice-transiting vessels is one of th￡ important issues concerning th￡ design of ships with ice classes. In this study, th￡ development of GUI software for estimation of ice resistance on ice-transiting vessels is discussed. lee resistance estimation equations, based on model tests and full-scale sea trial data from many previous research articles, are studied in conjunction with two ship categories i.e., ,icebreakers/supply/tug vessels and ice-strengthened cargo vessels. lee resistance estimation equations are summarized in common format and are compared with each other. The GUI software 'Ice View,' written in MS Visual Basic language, can calculate ice resistances according to varying ice thickness and ship speed. The software can provide the calculated results, with suitable tables and graphs, for easy comparison of each ice resistance estimation equation.

• Journal of Information Technology Services
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• v.5 no.1
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• pp.97-109
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• 2006

As software becomes more complex and its scope dramatically increases, the importance of research on developing methods for estimating software development efforts has been increased. Such accurate estimation has a prominent impact on the development projects. To develop accurate effort estimation models, many studies have been conducted among the academia and the practitioners. Out of the numerous methods, Constructive Cost Model (COCOMO) based on Line of Code (LOC), Regression Model based on Function Point (FP) were the most popular models in the past. As today's development environments are dynamically changing, these traditional methods do not work anymore. There is an impending need to develop an accurate estimation model which accommodates itself to the new environments. As a possible solution, this research proposes and evaluates an software development estimation model based on function points and neural networks.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.603-612
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• 2002

Area of software measurement in software engineering is active more than thirty years. There is a huge collection of researches but still no concrete software development effort and cost estimation model. If we want to measure the effort and cost of a software project, we need to estimate the size of the software. A number of software metrics are identified in the literature; the most frequently cited measures are LOC (line of code) and FPA (function point analysis). The FPA approach has features that overcome the major problems with using LOC as a measure of system size. This paper presents simple linear regression model that related software development effort to software size measured in FP. The model is derived from the plotting of the effort and FP relation. The experimental data are collected from 789 software development projects that were recently developed under the various development environments and development methods. Also, the model is compare with other regression analysis model. The presented model has the best estimation ability among the software effort estimation models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.133-141
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• 2001

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However is we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set. eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case, set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.