AdamOptimizer for the Optimisation of Use Case Points Estimation

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

doi:10.1007/978-3-030-63322-6_63

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…Hoc et al proposed the Adj-Effort approach to optimize effort estimation in terms of FPA based on the ISBSG release 2020 [27]. They adopted Multiple Linear Regression based on AdamOptimizer [28] with 10-Fold cross-validation to optimize the effort estimation. Their findings were compared with baseline models (Casper-Jones, and FPA-IFPUG) in terms of MMRE, Mean Absolute Error (MAE), and prediction level at 0.25 (PRED(0.25)).…”

Section: Related Workmentioning

confidence: 99%

Comparing Multiple Linear Regression, Deep Learning and Multiple Perceptron for Functional Points Estimation

et al. 2022

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This study compares the performance of Pytorch-based Deep Learning, Multiple Perceptron Neural Networks with Multiple Linear Regression in terms of software effort estimations based on function point analysis. This study investigates Adjusted Function Points, Function Point Categories, Industry Sector, and Relative Size. The ISBSG dataset (version 2020/R1) is used as the historical dataset. The effort estimation performance is compared among multiple models by evaluating a prediction level of 0.30 and standardized accuracy. According to the findings, the Multiple Perceptron Neural Network based on Adjusted Function Points combined with Industry Sector predictors yielded 53% and 61% in terms of standardized accuracy and a prediction level of 0.30, respectively. The findings of Pytorch-based Deep Learning are similar to Multiple Perceptron Neural Networks, with even better results than that, with standardized accuracy and a prediction level of 0.30, 72% and 72%, respectively. The results reveal that both the Pytorch-based Deep Learning and Multiple Perceptron model outperformed Multiple Linear Regression and baseline models using the experimental dataset. Furthermore, in the studied dataset, Adjusted Function Points may not contribute to higher accuracy than Function Point Categories.

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Section: Related Workmentioning

confidence: 99%

Comparing Multiple Linear Regression, Deep Learning and Multiple Perceptron for Functional Points Estimation

et al. 2022

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“…Moreover, these approaches showed promoting results by expanding the complexity weight in the original UCP method. Despite Nhung et al [41] and Hoc et al [54] conducted the optimization in UCP. However, they do not explore the potential of continuous complexity weight level in terms of optimization function.…”

Section: Related Workmentioning

confidence: 99%

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Ardiansyah

Ferdiana

Permanasari

2022

Int. J. Adv. Intell. Informatics

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Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant.

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“…Most algorithmic methods might mainly focus on optimizing factors such as the proportion of effort estimation known as Productivity Delivery Rate (PDR) or environmental factors that impact the development effort [5,[7][8][9]. These indicators impact the measurement of software effort.…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain those targets, scientists proposed many approaches to compute the predicted results and try to fit with the actual value. In their researches, some applied regression models, such as least squares regression, multiple linear regression [5,[7][8][9]. The other integrated regression methods work with fuzzy models, deep learning techniques [9,10], etc.…”

Section: Introductionmentioning

confidence: 99%

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An Approach to Adjust Effort Estimation of Function Point Analysis

Hoc

Hai

Nhung

2021

Software Engineering and Algorithms

Self Cite

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This study presents a modified approach to adjust a software development effort estimation. The AdamOptimizer-based regression model is adopted to adjust and enhance the accuracy of effort estimation. This approach is derived into three phases. The first step deals with the logarithmized formula of effort estimation computed by Function Point Analysis and Productivity Delivery Rate. The Adam-Optimizer-based regression model is examined in the second phase, and the ISBSG repository 2020 release R1 is considered as a historical dataset in this paper. Moreover, the K-Fold cross-validation technique is adopted to tunning the training model. In the following phase, all results are evaluated by statistical significance and the goodness of fit measure. Finally, a proposed approach is compared with others: Capers Jones, and the Mean Effort.

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AdamOptimizer for the Optimisation of Use Case Points Estimation

Cited by 10 publications

References 15 publications

Comparing Multiple Linear Regression, Deep Learning and Multiple Perceptron for Functional Points Estimation

Comparing Multiple Linear Regression, Deep Learning and Multiple Perceptron for Functional Points Estimation

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

An Approach to Adjust Effort Estimation of Function Point Analysis

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