Abstract
Software engineering projects can be complex and involve many different tasks and activities, such as collecting requirements, design, coding, testing, and deployment. Estimating the effort needed for the project is one of the first phases in the creation of software projects. For a software engineering project to be successfully completed, the amount of work needed to execute it must be precisely estimated. The paper suggests a novel method to calculate the effort needed to construct a software project by employing a heterogeneous stacked ensemble comprising of two base learners, namely Extreme Learning Machine (ELM) and Support Vector Regressor (SVR) and it also aims to investigate the effectiveness of Synthetic Minority Over-Sampling Technique for Regression (SMOTER) in predicting software project effort. The results indicate that the proposed model enhances the performance of the base models, resulting in a reduction of 35.7% in MAE, 24.1% in RMSE, and 18.3% in R-value. After the implementation of SMOTER, a noteworthy reduction in the error of the proposed model has been observed.
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References
De Carvalho, H.D.P., Fagundes, R., Santos, W.: Extreme learning machine applied to software development effort estimation. IEEE Access 9, 92676–92687 (2021). https://doi.org/10.1109/ACCESS.2021.3091313
Goyal, S.: Effective software effort estimation using heterogenous stacked ensemble. In: 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Thiruvananthapuram, India, pp. 584–588 (2022). https://doi.org/10.1109/SPICES52834.2022.9774231
Jawa, M., Meena, S.: Software effort estimation using synthetic minority over-sampling technique for regression (SMOTER). In: 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, pp. 1–6 (2022). https://doi.org/10.1109/INCET54531.2022.9824043
Zakrani, A., Najm, A., Marzak, A.: Support vector regression based on grid-search method for agile software effort prediction. In: 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), Marrakech, Morocco, pp. 1–6 (2018). https://doi.org/10.1109/CIST.2018.8596370
Shukla, S., Kumar, S., Bal, P.R.: Analyzing effect of ensemble models on multi-layer perceptron network for software effort estimation. In: 2019 IEEE World Congress on Services (SERVICES), Milan, Italy, pp. 386–387 (2019). https://doi.org/10.1109/SERVICES.2019.00116
Pressman, R.S.: Software Engineering: A Practitioner’s Approach, 7th edn. McGraw Hill Education, New York (2010)
Ahmad, F.B., Ibrahim, L.M.: Software development effort estimation techniques using long short-term memory. In: 2022 International Conference on Computer Science and Software Engineering (CSASE), Duhok, Iraq, pp. 182–187 (2022). https://doi.org/10.1109/CSASE51777.2022.9759751
Sehra, S.K., Kaur, J., Brar, Y.S., Kaur, N.: Analysis of data mining techniques for software effort estimation. In: 2014 11th International Conference on Information Technology: New Generations, Las Vegas, NV, USA, pp. 633–638 (2014). https://doi.org/10.1109/ITNG.2014.116
Van Hai, V., Nhung, H.L.T.K., Prokopova, Z., Silhavy, R., Silhavy, P.: Toward improving the efficiency of software development effort estimation via clustering analysis. IEEE Access 10, 83249–83264 (2022). https://doi.org/10.1109/ACCESS.2022.3185393
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Durgesh, D.V.S., Saket, M.V.S., Ramana Reddy, B. (2023). Improving Software Effort Estimation with Heterogeneous Stacked Ensemble Using SMOTER over ELM and SVR Base Learners. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_41
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DOI: https://doi.org/10.1007/978-3-031-36402-0_41
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