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A pragmatic ensemble learning approach for effective software effort estimation

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Abstract

The immense increase in software technology has resulted in the convolution of software projects. Software effort estimation is fundamental to commence any software project and inaccurate estimation may lead to several complications and setbacks for present and future projects. Several techniques have been following for ages of the software effort estimation. As the application of software is extensively increased in its size and complexity, the traditional methods aren’t adequate to meet the requirements. To achieve the accurate estimation of software effort, in this paper, a gradient boosting regressor model is proposed as a robust approach. The performance is compared with regression models such as stochastic gradient descent, K-nearest neighbor, decision tree, bagging regressor, random forest regressor, Ada-boost regressor, and gradient boosting regressor by employing COCOMO’81 containing 63 projects and CHINA of 499 projects. The regression models are evaluated by the evaluation metrics such as MAE, MSE, RMSE, and R2. From the results, it is evident that the gradient boosting regressor model is performing well by obtaining an accuracy of 98% with COCOMO’81 and 93% with CHINA dataset. The proposed method significantly performs better than all regression models used in comparison with both the datasets.

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Abbreviations

SGD:

Stochastic gradient descent

KNN:

K-nearest neighbor

DT:

Decision trees

BR:

Bagging regressor

RFR:

Random forest regressor

ABR:

Ada-Boost regressor

GBR:

Gradient boosting regressor

MRE:

Magnitude relative error

MMRE:

Mean magnitude of relative error

RMSE:

Root mean square error

MAE:

Mean absolute error

MdMRE:

Median magnitude of relative error

MSE:

Mean square error

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Authors and Affiliations

  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, 768018, India

    P. Suresh Kumar & H. S. Behera

  2. Department of CSE, Aditya Institute of Technology and Management (AITAM), Tekkali, 532201, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, 768018, India

    Bighnaraj Naik

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  1. P. Suresh Kumar
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  2. H. S. Behera
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  3. Janmenjoy Nayak
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  4. Bighnaraj Naik
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Correspondence to P. Suresh Kumar.

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The authors declare that this manuscript has no conflict of interest with any other published source and has not been published previously (partly or in full). No data have been fabricated or manipulated to support our conclusions.

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Suresh Kumar, P., Behera, H.S., Nayak, J. et al. A pragmatic ensemble learning approach for effective software effort estimation. Innovations Syst Softw Eng 18, 283–299 (2022). https://doi.org/10.1007/s11334-020-00379-y

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