• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.173-182
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• 2004

The research to estimate development effort of software has been progress. But, it is not easy gain that testing data for estimating of development effort. Also, if we get the testing data, it is important that analysis testing data. In this paper, we study the data analysis of software development effort using the 789 software development projects which developed in the 1990's. Software development scale and software development team site are various. Using the characteristic of factor, we have to study characteristic of data and we estimate the development effort step by step. First, we prove the difference of development effort with the 789 project data according to development type, development environment, the development language etc. Also, we execute the crosstabs analysis that team site and function point.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.375-375
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• 2000

Estimating software development effort remains a complex problem attracting considerable research attention. Improving the estimation techniques available to project managers would facilitate more effective control of time and budgets in software development as well as market. However, estimation is difficult because of its similarity to export judgment approaches and fur its potential as an expert assistant in support of human judgment. Especially, in software development for DCS (Distributed Control System), because of infrastructure software related to target-machines hardware and process characteristics should be considered, estimating software development effort is more complex. This paper suggests software development effort estimation technique using neural network. The methods considered are based on COCOMO and case-based projects. Estimation results applied to case-based project appeared to have value fur software development effort estimation models.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.865-876
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• 2002

Successful project planning relies on a good estimation of the effort required to complete a project, together with the schedule options that may be available. Existing software effort estimation models are present only the total effort and instantaneous effort function for the software life-cycle. Also, Putnam presents constant effort rate in each phase. However, the size of total effort varies according to the software projects under the influence of its size, complexity and operational environment. As a result, the allocated effort in each phase also differs from one project to another. This paper suggests models for effort allocation in planning, specifying, building, testing and implementing phases followed by the project size and development types. These models are derived from 155 different projects. Therefore, these models can be considered as a practical guideline in management of project schedule and effort allocation.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1483-1490
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• 2004

To accomplish a project successfuly, we have to estimate develpment effort accurately. But, development effort is different to software size and operation environment. Usually, we made use of function point for estimating development effort. In this paper. we make use of 789 project data. It is related to development projects in 1990`s. We investigate the variable affecting development effort. Also, we exedcute multiple liner regression analysis for looking linear relation about variables. We find the regression equation for multistage by dividing PDR that influ-enced development effort step by step.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.20-28
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• 2004

Estimation of software project cost, effort, and duration in the early stage of software development cycle is a difficult and key problem in software engineering. Most of development effort and duration estimation models presented by regression model of simple relation function point vs. effort and effort vs. duration instead of considering developer's productivity. But different project have need for different effort according to developer's productivity if the projects are same software size. Also, different duration takes according to developer's productivity if the projects require the same effort. Therefore, models that take into account of productivity have a limited application in actual development project. This paper presents models that can be estimate the duration according to productivity in order to compensate a shortcoming of the previous models. Propose model that could presume development period by various methods based on productivity and compared models' performance. As a result of performance comparison, an estimating model of development period from software size got simple and most good result. The model gives decision-making information of development duration to project management in the early stage of software life cycle.

• Journal of the Korea Computer Industry Society
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• v.3 no.3
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• pp.295-306
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• 2002

Successful project planning relies on a good estimation of the effort required to complete a project, together with the schedule options that may be available. Despite the extensive research done developing new and better models, existing software effort estimation models are present only the total effort and instantaneous effort function for the software life-cycle. Also, Putnam presents constant effort rate in each phase. However, the size of total effort are variable according to the software projects under the influence of its size, complexity and operational environment. As a result, the allocated effort in each phase also differ from project to project. This paper suggests the criteria for effort allocation in planning, specifying, building, testing and implementing phase followed by the project total effort. These criteria are derived from 183 different projects. This result can be considered as a practical guideline in management of project schedule and effort allocation.

• Journal of Internet Computing and Services
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• v.14 no.1
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• pp.23-30
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• 2013

Test effort estimated so far is as a by-product of the development effort estimation activity which is based on the FP, UCP, COCOMO model, or calculated data from the project knowledge base which is containing test effort information for the test phase on software development life cycle. In this paper, test effort estimation model and calculating procedures are suggested, which is independent from software development effort estimation model. Generally test efforts is depends on the number and the complexity of test cases, and also maturity of test organization that performs test activities, such as integration test, system test, acceptance test and so on. The estimated results with the suggested test effort estimation model has deviation of 4.7% compare to the corresponding test efforts generated by the development effort estimationprocedures. The suggesting model will be accurate more and more with refinements of coefficients which reflect the technical and environmental maturity level of test organization, and also including the software complexity level of projects.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.581-586
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• 2001

An increasingly important facet of software development is the ability to estimated the associated coast and effort of development early in the development life cycle. In spite of the most generally sued procedures for estimation of the software development effort and cost were linear regression analysis. As a result of the software complexity and various development environments, the software effort and cost estimates that are grossly inaccurate. The application of nonlinear methods hold the greatest promise for achieving this objects. Therefore this paper presents an RBF (radial basis function) network model that is able to represent the nonlinear relation for software development effort, The research describes appropriate RBF network modeling in the context of a case study for 24 software development projects. Also, this paper compared the RBF network model with a regression analysis model. The RBF network model is the most accuracy of all.

• 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.

• Journal of the Korea Computer Industry Society
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• v.2 no.6
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• pp.859-866
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• 2001

Successful project planning relies on a good estimation of the effort required to complete a project, together with the schedule options that may be available. Despite the extensive research done developing new and better models, existing software effort estimation models are present only the total effort and effort (or manpower： people per unit time) function for the software life-cycle. Also, Putnam presents constant effort rate in each subcycles. However, the size of total efforts are variable according to the software projects under the influence of its size, complexity and operational environment. As a result, the allocated effort in subcycle also differ from project to project. This paper suggests the linear and polynomial effort estimation models in specifying, building and testing phase followed by the project total effort. These models are derived from 128 different projects. This result can be considered as a practical guideline in management of project schedule and effort allocation.