• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1356-1376
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• 2023

Software effort estimation is one of the most difficult tasks in software development whereas predictability is also of equal importance for strategic management. Accurate prediction of the actual cost that will be incurred in software development can be very beneficial for the strategic management. This study discusses the latest trends in software estimation focusing on analogy-based techniques to show how they have improved the accuracy for software effort estimation. It applies the standard deviation technique to the expected value of analogy-based estimates to improve accuracy. In more than 60 percent cases the applied technique of this study helped in improving the accuracy of software estimation by reducing the Magnitude of Relative Error (MRE). The technique is simple and it calculates the expected value of cost or time and then uses different confidence levels which help in making more accurate commitments to the customers.

• 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.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.589-599
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• 2008

Accurate software effort estimation has always been a challenge for the software industrial and academic software engineering communities. Many studies have focused on effort estimation methods to improve the estimation accuracy of software effort. Although data quality is one of important factors for accurate effort estimation, most of the work has not considered it. In this paper, we investigate the influence of outlier elimination on the accuracy of software effort estimation through empirical studies applying two outlier elimination methods(Least trimmed square regression and K-means clustering) and three effort estimation methods(Least squares regression, Neural network and Bayesian network) associatively. The empirical studies are performed using two industry data sets(the ISBSG Release 9 and the Bank data set which consists of the project data collected from a bank in Korea) with or without outlier elimination.

• 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.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.376-376
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• 2000

Several algorithmic models have been proposed to estimate software cost and other management parameters. In particular, early prediction of completion time is absolutely essential for proper advance planning and a version of the possible ruin of a project. However, estimation is difficult because of its similarity to export judgment approaches and for its potential as an expert assistant in support of human judgment. Especially, the nature of the Norden/Rayleigh curve used by Putnam, renders it unreliable during the initial phases of the project, in projects involving a fast manpower buildup, as is the case with most software projects. Estimating software development effort is more complexity, because of infrastructure software related to target-machines hardware and process characteristics should be considered in software development for DCS (Distributed Control System). In this paper, we propose software development effort estimation technique using adaptive neural fuzzy inference system. The methods is applied to case-based projects and discussed.

• 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.

• Journal of Korea Multimedia Society
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• v.13 no.11
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• pp.1575-1585
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• 2010

It is very important to forecast a back resource of a software development effort at the early stage of development life cycle for successful project processing, and it is carried out through software size estimation. The recent trend of software size estimation method is focused on the user's value such as FPA. We measure the actual development effort through case study and calculate CFP directly according to the cosmic-ffp manual V.3.0. in this paper. We also propose the software development effort estimation model by using the produced data. COSMIC-FFP does not use weights of necessary function elements, and so it has disadvantage in estimating sizes. This paper proposes the estimation model to estimate the precision software size by using system complexity as weight.