Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation

Hoc, Huynh Thai; Silhavy, Petr; Fajkus, Martin; Prokopová, Zdenka; Šilhavý, Radek

doi:10.3390/math10244649

Cited by 5 publications

(6 citation statements)

References 38 publications

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“…Besides that, the absolute valuable information related to MMRE and PRED(0.25) (sig Left , sig Right , sig) attained from PytEffort might be slightly smaller than that from EnsEffort/Group 1. As mentioned in [63], PytEffort might be more stable than EnsEffort/Group 1. Additionally, the p-value computed by Mann-Whitney Utest between EnsEffort/Group 1 and PytEffort is less than 0.05, demonstrating that there is a statistically significant difference in the medians between the two methodologies.…”

Section: Resultsmentioning

confidence: 86%

“…Evaluation measures are used to determine how effective the proposed approach is. Evaluation measures such as MBRE, MIBRE, MAE, SA, and MMRE, PRED(0.25) with their useful information were utilized, as previously reported [4], [59], [62], [63]. Because of this, the results of the experiments in this research can be applied to a much larger group of projects.…”

Section: F Validity Evaluationmentioning

confidence: 99%

“…Other metrics used to assess effort estimation accuracy include PRED(x) (8), MAE (9), and standardised accuracy (SA) [62] (10), Mean Balance Relative Error (MBRE) (11), and Mean Inverted Balance Relative Error (MIBRE) (12). Furthermore, this study includes helpful information on MMRE and PRED (0.25) [63], those are, sig Left (14), sig Right (15), and sig (16). VOLUME 11, 2023 Sign values are determined using (13).…”

Section: Performance Metricsmentioning

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: Resultsmentioning

confidence: 86%

Section: F Validity Evaluationmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

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Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

et al. 2023

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“…(m)), Accuracy, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE). Detailed descriptions of the measures, as well as the reasons for their use, are given in [7,21,36].…”

Section: Prediction Accuracymentioning

confidence: 99%

Advanced Bayesian Network for Task Effort Estimation in Agile Software Development

Turic,

Celar,

Dragicevic

et al. 2023

Applied Sciences

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Effort estimation is always quite a challenge, especially for agile software development projects. This paper describes the process of building a Bayesian network model for effort prediction in agile development. Very few studies have addressed the application of Bayesian networks to assess agile development efforts. Some research has not been validated in practice, and some has been validated on one or two projects. This paper aims to bring the implementation and use of Bayesian networks for effort prediction closer to the practitioners. This process consists of two phases. The Bayesian network model for task effort estimation is constructed and validated in the first phase on real agile projects. A relatively small model showed satisfactory estimation accuracy, but only five output intervals were used. The model was proven to be useful in daily work, but the project manager wanted to obtain more output intervals, although increasing the number of output intervals reduces the prediction accuracy. In the second phase, the focus is on increasing the number of output intervals while maintaining satisfactory accuracy. The advanced model for task effort estimation is developed and tested on real projects of two software firms.

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“…In software estimation, the mean of magnitude relative error (MMRE) [30,31], and Pred (x) [31] be used for the evaluation of the most likely estimate of effort. Meanwhile, magnitude relative error (MRE) is obtained using Eq.…”

Section: Comparison With Ufp Input On Cocomo IImentioning

confidence: 99%

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

2023

IJIES

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In COCOMO II, the primary input for estimating development effort in person month (PM), duration, and cost is the size of the software. Until now, there are two ways to get the size, namely (1) size is estimated using a line of code of software, and (2) size is estimated using unadjusted function points (UFP), which is one of the functional size measurements (FSM). In this study, we added a new way to obtain the size as the primary input in COCOMO II, namely with COSMIC function points (CFP). CFP has several advantages compared to other FSMs, including UFP. Therefore, like UFP, CFP is converted first to LOC, so the conversion equation must be obtained first. We applied four models to get the conversion functions: Ordinary least squares regression (OLSR), support vector regression (SVR) with linear, polynomial, and Gaussian kernel functions. The four models were applied using a dataset from small-scale business application software in Java. The results showed that PM estimation using the CFP model as the primary input produced better accuracy based on MMRE and Pred (0.25), namely 17%-19% and 67%-80%, than the UFP model on the COCOMO II of 135% and 10%.

show abstract

Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation

Cited by 5 publications

References 38 publications

Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

Advanced Bayesian Network for Task Effort Estimation in Agile Software Development

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

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