An Approach to Adjust Effort Estimation of Function Point Analysis

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

doi:10.1007/978-3-030-77442-4_45

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…In 2021, Hoc et al [26] developed the Adj-Effort method to optimize effort estimates in terms of FPA on the basis of the ISBSG version 2020. In order to get the most accurate estimate possible, they used a method called MLR based on AdamOptimizer [10] with 10-fold cross-validation.…”

Section: Related Workmentioning

confidence: 99%

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Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

et al. 2023

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This study focuses on improving the accuracy of effort estimation by employing ensemble, deep learning, and transfer learning techniques. An ensemble approach is utilized, incorporating XGBoost, Random Forest, and Histogram Gradient Boost as generators to enhance predictive capabilities. The performance of the ensemble method is compared against both the deep learning approach and the PFA-IFPUG technique. Statistical criteria including MAE, SA, MMRE, PRED(0.25), MBRE, MIBRE, and relevant information related to MMRE and PRED(0.25) are employed for evaluation. The results demonstrate that combining regression models with Random Forest as the final regressor and XGBoost and Histogram Gradient Boost as prior generators yields more accurate effort estimation than other combinations. Furthermore, the findings highlight the potential of transfer learning in the deep learning method, which exhibits superior performance over the ensemble approach. This approach leverages pre-trained models and continuously improves performance by training on new datasets, providing valuable insights for cross-company and cross-time effort estimation problems. The ISBSG dataset is used to build the pretrained model, and the inductive transfer learning approach is verified based on the Desharnais, Albrecht, Kitchenham, and China datasets. The study underscores the significance of transfer learning and the integration of domain-specific knowledge from existing models to enhance the performance of new models, thereby improving accuracy, reducing errors, and enhancing predictive capabilities in effort estimation.INDEX TERMS Software effort estimation, ensemble, function point analysis, deep learning, inductive transfer learning.

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Section: Related Workmentioning

confidence: 99%

“…We used 10-fold cross-validation to compare different adaption strategies. The primary reason is because 10-fold cross-validation has been used in prior research and is suggested by [4], [26], and [27] for comparing effort estimate models.…”

Section: Threats To Validitymentioning

confidence: 99%

Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

et al. 2023

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“…Casper-Jones presented a rule (5) that might be the suitable solution to measure the software effort in the project's early stages when the productivity value is unknown [25]. It will be used as a baseline model for comparison.…”

Section: Table 2 General Systems Characteristicsmentioning

confidence: 99%

“…We used the 10-fold crossvalidation strategy to compare various adaptation techniques. The main reason is that 10-fold cross-validation has previously been used in studies and is recommended by [18], [25], and [29] for comparing effort estimation models.…”

Section: Threats To Validity a Internal Threatsmentioning

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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Improving the Performance of Effort Estimation in Terms of Function Point Analysis by Balancing Datasets

Hoc

Hai

Nhung

et al. 2023

Software Engineering Application in Systems Design

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An Approach to Adjust Effort Estimation of Function Point Analysis

Cited by 4 publications

References 24 publications

Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

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

Improving the Performance of Effort Estimation in Terms of Function Point Analysis by Balancing Datasets

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