A pragmatic ensemble learning approach for effective software effort estimation

Kumar, P. Suresh; Behera, H. S.; Nayak, Janmenjoy; Naik, Bighnaraj

doi:10.1007/s11334-020-00379-y

Cited by 25 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, proposed MoWE model is compared with other heterogenous ensemble methods found in literature, including gradient boosting [ 75 ] stacking [ 76 ] majority voting and weighted ensemble (with non-optimized weights) [ 30 ]. Further, performance comparison of our proposed MoMdbWE techniques is made with previous EEE studies ( Table 11 ).…”

Section: Methodsmentioning

confidence: 99%

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

View full text Add to dashboard Cite

Software development effort estimation (SDEE) is recognized as vital activity for effective project management since under or over estimating can lead to unsuccessful utilization of project resources. Machine learning (ML) algorithms are largely contributing in SDEE domain, particularly ensemble effort estimation (EEE) works well in rectifying bias and subjectivity to solo ML learners. Performance of EEE significantly depends on hyperparameter composition as well as weight assignment mechanism of solo learners. However, in EEE domain, impact of optimization in terms of hyperparameter tunning as well as weight assignment is explored by few researchers. This study aims in improving SDEE performance by incorporating metaheuristic hyperparameter and weight optimization in EEE, which enables accuracy and diversity to the ensemble model. The study proposed Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE) approach. This is achieved by proposed search space division and hyperparameter optimization method named as Multi-dimensional bagging (Mdb). Metaheuristic algorithm considered for this work is Firefly algorithm (FFA), to get best hyperparameters of three base ML algorithms (Random Forest, Support vector machine and Deep Neural network) since FFA has shown promising results of fitness in terms of MAE. Further enhancement in performance is achieved by incorporating FFA-based weight optimization to construct Metaheuristic-optimized weighted ensemble (MoWE) of individual multi-dimensional bagging schemes. Proposed scheme is implemented on eight frequently utilized effort estimation datasets and results are evaluated by 5 error metrices (MAE, RMSE, MMRE, MdMRE, Pred), standard accuracy and effect size along with Wilcox statistical test. Findings confirmed that the use of FFA optimization for hyperparameter (with search space sub-division) and for ensemble weights, has significantly enhanced performance in comparison with individual base algorithms as well as other homogeneous and heterogenous EEE techniques.

show abstract

Section: Methodsmentioning

confidence: 99%

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…However, it is highly subjective and may be affected by biases or limitations in the knowledge and experience of the experts involved. Machine learning algorithms have also been used for cost estimation in software development, with promising results (Kumar et al, 2021;Zhao & Zhang, 2020;Panda & Majhi, 2020;Promise Software Engineering Repository, n.d.). These algorithms can analyze large amounts of data and identify patterns and relationships that are not easily discernible through other approaches.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments

Alauthman,

al-Qerem,

Alangari

et al. 2023

International Journal of Software Science and Computational Intelligence

View full text Add to dashboard Cite

Cost estimation for software development is crucial for project planning and management. Several regression models have been developed to predict software development costs, using historical datasets of previous projects. Accurate cost estimation in software development is heavily influenced by the relevance and quality of the cost estimation dataset and its suitability to the software development environment. The currently available cost estimation datasets are limited to North American and European environments, leaving a gap in the representation of other economically and technically constrained software industries. In this article, the authors evaluate the performance of regression models using the SEERA dataset, which highly represents these constrained environments. This study provides insights into selecting regression models for cost estimation in software development. It highlights the importance of using appropriate models based on the specific software development model and dataset used in the estimation process. In the performance evaluations of eight regression models, including elastic net, lasso regression, linear regression, neural network, RANSACRegressor, random forest, ride regression, and SVM, for cost estimation in different software models, along with correlation coefficients and accuracy indicators, were reported. The results showed that SVM and random forest indicated superior performance. However, the elastic net, lasso regression, linear regression, neural network, and RANSACRegressor models also demonstrated exemplary performance in cost estimation.

show abstract

“…Hidmi and Sakar 34 used ensemble learning by combining SVM and kNN models to estimate the effort on Desharnais and Maxwell data; the authors validated the results and identified kNN outperformed and ensemble learning improved the results. Suresh Kumar et al 35 used gradient boosting, an ensemble learning algorithm for effort estimation on COCOMO81 and China datasets, and obtained

R^{2}

values of 98% and 93%, respectively.…”

Section: Related Workmentioning

confidence: 99%

Towards effective feature selection in estimating software effort using machine learning

Jadhav

Shandilya

2023

J Software Evolu Process

View full text Add to dashboard Cite

Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets.

show abstract

A pragmatic ensemble learning approach for effective software effort estimation

Cited by 25 publications

References 31 publications

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments

Towards effective feature selection in estimating software effort using machine learning

Contact Info

Product

Resources

About