A Review of Use Case-Based Development Effort Estimation Methods in the System Development Context

Nhung, Ho Le Thi Kim; Hoc, Huynh Thai; Hai, Vo Van

doi:10.1007/978-3-030-30329-7_44

Cited by 13 publications

(11 citation statements)

References 23 publications

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“…In publications [7,8,16], the mathematical equation used to estimate the size of the project is presented as Eq.8; where TUAW is Total Unadjusted Actor Weight [7,8,16]; UUCW is the Unadjusted Use Case Weight [7,8,16]; TCF is the Technical Complexity Factor [7,8,16]; and ECF is the Environmental Complexity Factor [7,8,16].…”

Section: Our Proposed Methods -Adamucpmentioning

confidence: 99%

“…The Mean Magnitude of Relative Error -(MMRE) and the Prediction level -(PRED(x) are well-known evaluation criteria. All criteria were employed in order to examine the models´ prediction accuracy [7,16,4]. Both parameters are based on the quantity call Magnitude of Relative Error (MRE) method.…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…The Residual Sum of Squares -(RSS), is a metric in regression analysis used to measure modeling error variation [7], it is also known as the Sum of Square Residuals -(SSR) or the Sum of Squared Estimate of Errors -(SSE) method. The coefficient of determination (R 2 ) is then used to assess the "goodness-of-fit" measure in the regression model [7,16,4]. After fitting the regression model, we use R 2 to determine how well the model fits the dataset.…”

Section: Evaluation Criteriamentioning

confidence: 99%

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AdamOptimizer for the Optimisation of Use Case Points Estimation

Hoc

Hai

Nhung

2020

Advances in Intelligent Systems and Computing

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Use Case Points is considered to be one of the most popular methods to estimate the size of a developed software project. Many approaches have been proposed to optimise Use Case Points. The Algorithmic Optimisation Method uses the Multiple Least Squares method to improve the accuracy of Use Case Points by finding optimal coefficient regressions, based on the historical data. This paper aims to propose a new approach to optimise the Use Case Points method based on Gradient Descent with the support of the TensorFlow package. The significance of its purpose is to conduct a new approach that might lead to more accurate prediction than that of the Use Case Points and the Algorithmic Optimisation Method. As a result, this new approach outweighs both the Use Case Points and the Algorithmic Optimisation Methods.

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Section: Our Proposed Methods -Adamucpmentioning

confidence: 99%

Section: Evaluation Criteriamentioning

confidence: 99%

Section: Evaluation Criteriamentioning

confidence: 99%

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AdamOptimizer for the Optimisation of Use Case Points Estimation

Hoc

Hai

Nhung

2020

Advances in Intelligent Systems and Computing

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“…It gives project managers good controlling decisions to ensure that the right amount of effort is allocated to the various activities during the project's development life cycle. As a result, this has led many researchers to investigate software estimation for more accurate SDEE methods [5], [6]. Existing research efforts related to SDEE can be classified into three main groups [7]- [9]: 1.…”

Section: Introductionmentioning

confidence: 99%

Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

et al. 2022

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“…Several researchers have expressed interest in Use Case-based approaches along with their initial applicability. For this reason, many methods have been proposed [6,8]. These methods are suitable for different complexity, type, domain, environments, software, etc.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Technical and Environmental Complexity Factors for Improving Use Case Points Estimation

Nhung

Hoc

Hai

2020

Advances in Intelligent Systems and Computing

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This paper presents a proposed method for improving the prediction ability of the Use Case Points method. Our main goal is to use the Least Absolute Shrinkage and Selection Operator Regression methods to find out which of the technical and environmental complexity factors significantly affect the accuracy of the Use Case Points method. Two regression models were used to calculate the selected significant variables. The results of several evaluation measures show that the proposed estimation method ability is better than the original Use Case Points method. The Sum of Squared Error of the proposed method is better than the results obtained by the original one. The study also enables project managers to understand how to assess the technical and environmental complexity factors better -since they do have an important impact on effort estimation.

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A Review of Use Case-Based Development Effort Estimation Methods in the System Development Context

Cited by 13 publications

References 23 publications

AdamOptimizer for the Optimisation of Use Case Points Estimation

AdamOptimizer for the Optimisation of Use Case Points Estimation

Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

An Evaluation of Technical and Environmental Complexity Factors for Improving Use Case Points Estimation

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