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

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

doi:10.1007/978-3-031-21435-6_60

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…This process involves an in-depth analysis of how each activity relates to these components [13]. 3) Complexity Calculation for FPA Components: Each component of FPA (EI, EO, EQ, ILF, EIF) is then calculated based on its complexity: Simple, Average, or Complex [14]. Table 1 involves evaluating the level of difficulty, volume of data, and interaction with other components of the system.…”

Section: Methodsmentioning

confidence: 99%

Activity-based function point complexity of use case diagrams for software effort estimation

Jayadi,

Dewi,

Sussolaikah

2024

J. Soft Comput. Explor.

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This study proposes a Function Point Analysis (FPA) based software development effort estimation methodology integrated with Use Case Diagrams. These methods include identifying actor activities, classifying those activities into FPA categories, and calculating Unadjusted Function Points (UFP). Followed by the calculation of Technical Complexity Factors (TCF) and Adjusted Function Points (AFP), this study aims to produce more accurate man-hours estimates. Results show a UFP of 162 TCF of 11, AFP of 123.12, and an estimated effort of 1846.8 hours worked, while the actual effort is 1228 hours. Evaluation of estimates using the metrics Mean Magnitude of Relative Error (MMER) 0.34, Mean Magnitude of Relative Error (MMRE) 0.50, Mean Absolute Error (MAE) 618.80, Mean Balanced Relative Error (MBRE) 0.50, Mean Inverse Balanced Relative Error (MIBRE) 0.34, and Root Mean Squared Error (RMSE) 618.80, showed sufficient precision despite the overestimation. The study suggests the need for adjustments in TCF calculations and considering development environment factors in more detail to improve estimation accuracy. These findings are essential in improving the precision of effort estimation methodologies in software development, particularly in projects that use Use Case Diagrams as the primary framework.

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Section: Methodsmentioning

confidence: 99%

Activity-based function point complexity of use case diagrams for software effort estimation

Jayadi,

Dewi,

Sussolaikah

2024

J. Soft Comput. Explor.

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“…as Function Point Analysis (FPA) [4], [5]. They might make their decision based on the researchers' suggestions, which are based on information from sources that have already been used for the project.…”

Section: Introductionmentioning

confidence: 99%

“…There might be two popular kinds of approaches to propose the predictive models, including regression-based approaches [4], [10], [11], [12], [13], machine learningbased or deep learning-based approaches [4], [5], [14], [15]. Moreover, solo vs ensemble techniques is the concepts mentioned in the publication [16].…”

Section: Introductionmentioning

confidence: 99%

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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Agile effort estimation in Colombia: An assessment and opportunities for improvement

Cubillos,

Aponte,

Gomez

et al. 2024

Science of Computer Programming

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

Cited by 3 publications

References 12 publications

Activity-based function point complexity of use case diagrams for software effort estimation

Activity-based function point complexity of use case diagrams for software effort estimation

Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

Agile effort estimation in Colombia: An assessment and opportunities for improvement

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