• 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.

• 한국IT서비스학회:학술대회논문집
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• 2005.05a
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• pp.372-380
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• 2005

소프트웨어 개발노력 추정에 대한 연구는 소프트웨어가 복잡해지고 범위가 크게 증가함에 따라서 그 중은 지속적으로 부각되고 있다. 관련 프로젝트를 발주하는 업체나, 이를 수주하고 개발을 진행하는 업체에게 원가를 고려하는 측면에서 매우 중요한 부분을 차지하고 있다. 이러한 개발노력 추정을 위하여 다양한 접근 방식들이 고려되어지고 있는데, 그중에서 많이 활용되어지고 있는 방식은 소프트웨어 규모에 기반을 둔 LOC(Line Of Code) 기반 COCOMO (Constructive Cost Model) 모델이나 기능점수(Function Point)를 기반으로 한 회귀분석 모델, 인공지능(Artificial Intelligence)을 활용한 신경망(Neural Network) 모델, 사례분석기법 (CBR, Case Based Reasoning) 등이 있다. 이중에서 최근에 기능점수를 활용한 개발노력 추정에 관한 연구들이 활발히 진행되고 있으나 개발노력 추정에는 소프트웨어 규모의 척도인 기능점수 뿐만 아니라, 개발환경을 구성하는 여러 가지 측면에 대한 고려가 추가되어져야 한다. 이에 본 논문은 최신의 소프트웨어 개발 사례들에 대하여 기능점수 및 추가적인 개발환경 요소들을 면밀히 분석하고, 분석한 내용에 대해서 전문가들의 설문을 통한 빈도분석 및 로지스틱 회귀분석, 데이터마이닝 기법인 신경망 분석 등을 활용하여 개발노력 추정 모델을 구축함으로써, 소프트웨어 개발의 다양한 측면의 중요성을 강조하고, 정확한 추정의 방안을 제시 하고자 노력 하였다.

• 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.

• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.105-117
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• 2007

In order to succeed in a bid or development, the project manager should estimate its cost and schedule more accurately in the early stage of the project. Usually, the nominal schedule of most projects can be derived from rule of thumb, first-order estimation practice, or ball-park schedule estimation table. But the rule-of-thumb models for the nominal schedule estimation are so various, and the first-order estimation practice does not provide sufficient information. So they do not help much to decide on the proper development effort and schedule for a particular size of project. This paper presents a statistical regression model for deciding the development effort and schedule of a project using the ball-park schedule estimation table. First, we have redefined such words suggested in the ballpark schedule estimation table as shortest possible schedule, efficient schedule and nominal schedule, Next, we have investigated the relationship between the development effort and the schedule. Finally, we have suggested a model for estimating the development effort and the more accurate schedule of such particular sizes of software as are not presented in the ball-park schedule estimation table. The experimental results show that our proposed regression analysis model decreases the mean magnitude of relative error by 2% at maximum. Also this model can estimated the development effort and schedule for a particular size of software.

• 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.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.103-110
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• 2005

Software Development is converting from structural to object oriented method. The later software development prefers the iterative process applications, not aterfall process and based on use case model, the requirements are expressed and based on this, analysis, design and coding are accomplished. Therefore, size of the software to be developed is estimated basing on use case and it is only possible to maintain the project success by estimating development effort, cost and development period. Even though development effort estimation models related current use case point. there is no appropriate development effort estimating. This paper shows, as a result of applying the development effort estimating model about UCP to the growth curve, a superior performance improvement to current statistical models. Therefore, estimation of development effort by applying this model, project development maintenance can be appropriately carried out.

• 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.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.259-266
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• 2002

The main issue in software development is the ability of software project effort and cost estimation in the early phase of software life cycle. The regression models for project effort and cost estimation are presented by function point that is a software sire. The data sets used to conduct previous studies are of ten small and not too recent. Applying these models to 789 project data developed from 1990 ; the models only explain fewer than 0.53 $R^2$(Coefficient of determination) of the data variation. Homogeneous group in accordance with project delivery rate (PDR) divides the data sets. Then this paper presents general effort estimation models using project delivery rate. The presented model has a random distribution of residuals and explains more than 0.93 $R^2$ of data variation in most of PDR ranges.