Ensemble Effort Estimation using dynamic selection

Cabral, Jose Thiago H. de A.; Oliveira, Adriano L. I.

doi:10.1016/j.jss.2021.110904

Cited by 19 publications

(14 citation statements)

References 42 publications

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“…Single ML models were applied (i.e., NNs and SVR); however, the combination of ML models (termed ensemble) represents an important ongoing research topic because it has improved the performance of solo models. 72 Thus, future work will be approached to combine several single prediction models in their two types of categories: homogeneous and heterogeneous. The first mentioned refers to an ensemble that combines one base model with at least two different configurations or a combination of one ensemble learning model and one base model, whereas the latter involves an ensemble that combines at least two different base models.…”

Section: Discussionmentioning

confidence: 99%

Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

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The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR-SMO-PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR-SMO-PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε-SVR and ʋ-SVR in the seven data sets. Thus, the SVR-SMO-PUK is useful to software managers to predict CE.

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Section: Discussionmentioning

confidence: 99%

Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

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“…In a real-life scenario, when an individual needs to take a decision, then that individual will get the opinions of different people and ultimately take the decision. In similar manner, [14] Ensembling combines multiple learning algorithms and produces a reliable model to get a cooperative concert.…”

Section: Y=a+bx+ementioning

confidence: 99%

Software Effort Estimation using Base Ensembled Regression Techniques and Principal Components Regression as Super Learner

G¹,

2023

Preprint

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With the increased expectation of faster and more modern software products for the digital market, providing accurate, qualitative, costeffective and time-bound software is crucial. Effective Software Project planning and Estimations are vital steps for developing a software product. The most precise effort in person-months or person-hours is predicted by software effort estimate. Inaccurate Estimations of effort provided by several estimation techniques have led to complications in the implementation of project, budget and schedule. Regression methods were the most common statistical method used for predicting modelling. Ensemble approaches use multiple learning algorithms to provide a better predictive model than individual learners. Our proposed scheme is stacked ensemble technique that used Decision Tree, Principal Components Regression (PCR), Random Forest, NeurelNet, glmnet, XGBoost, Earth and Support Vector Machine as base ensembled regression techniques. PCR as a Super learner was shown to be the best technique for assessing effort after extensive testing with a variety of super learners. Estimators were evaluated using the Mean Absolute Error (MAE), the Root Mean Squared Error (RMSE), the Mean Squared Error (MSE), the percentage of close approximations within 25 Percent of the true values (PRED (25)), and the R-squared coefficients.

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“…Combination of ABE and PSO, 44 ABE and ANN, 45 and ABE and differential evolution‐based model 46 are other types of the ABE method. In order to achieve a high‐performance model, some studies have combined several estimation methods using specific selection methods or combination rules to estimate the effort 1,33,47–50 . Jose Thiago and Oliveira 50 have introduced a dynamic ensemble selection method of 15 regression models to obtain better accuracy in the predictions.…”

Section: Related Researchmentioning

confidence: 99%

“…In order to achieve a high‐performance model, some studies have combined several estimation methods using specific selection methods or combination rules to estimate the effort 1,33,47–50 . Jose Thiago and Oliveira 50 have introduced a dynamic ensemble selection method of 15 regression models to obtain better accuracy in the predictions. Many of the new methods that are introduced in recent studies are complicated and are not practical in the real world because they require many past project data and special skills and knowledge.…”

Section: Related Researchmentioning

confidence: 99%

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CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

Feizpour

Tahayori

Sami

2023

J Software Evolu Process

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Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has important effects on the success of software projects. COCOMO (Constructive Cost Model) is a parametric data‐driven SDEE model whose parameters must be calibrated with an organization's local data for accurate estimation. Such data are scarce for most organizations. On the other hand, CoBRA (Cost estimation, Benchmarking, and Risk Assessment) is one of the powerful hybrid methods that need a small number of local historical data for effort estimation. However, data gathering in CoBRA is time‐consuming and costly. To ease the use of CoBRA, in this paper, we design a methodology that extracts CoBRA‐required data from COCOMO datasets. By the proposed method, data collected for COCOMO would be used in CoBRA. Using CoBRA, a more accurate estimation of the required effort would be achieved with fewer number of historical data than what is required to calibrate the COCOMO model. We apply the proposed method on six well‐known public COCOMO datasets and use them in CoBRA. Obtained results depict an increase in the accuracy of estimations in comparison with other existing methods.

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Ensemble Effort Estimation using dynamic selection

Cited by 19 publications

References 42 publications

Effort prediction for the software project construction phase

Effort prediction for the software project construction phase

Software Effort Estimation using Base Ensembled Regression Techniques and Principal Components Regression as Super Learner

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

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