SE<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1338" altimg="si1.svg"><mml:msup><mml:mrow/><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>M: A model for software effort estimation using pre-trained embedding models

Fávero, Eliane Maria De Bortoli; Casanova, Dalcimar; Pimentel, Andrey Ricardo

doi:10.1016/j.infsof.2022.106886

Cited by 9 publications

(1 citation statement)

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“…An enhanced method for Scrum-based Agile projects using 36 success indicators, resulting in more cost-effective and efficient outcomes [7]. A utilized pre-trained embedding models to improve textual requirements gathering for effort estimation, achieving reliable and efficient results [8]. Applied machine learning and deep learning techniques to predict hardware development tasks' duration, demonstrating the applicability of software effort estimation in the hardware sector [9].…”

Section: Related Workmentioning

confidence: 99%

Numerical Simulation and Design of Ensemble Learning Based Improved Software Development Effort Estimation System

Gora,

Sinha

2023

IJRITCC

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This research paper proposes a novel approach to improving software development effort estimation by integrating ensemble learning algorithms with numerical simulation techniques. The objective of this study is to design an ensemble learning-based software development effort estimation system that leverages the strengths of multiple algorithms to enhance accuracy and reliability. The proposed system combines the power of ensemble learning, which involves aggregating predictions from multiple models, with numerical simulation techniques that enable the modelling and analysis of complex software development processes. A diverse set of software development projects is collected, encompassing various domains, sizes, and complexities. Ensemble learning algorithms such as Random Forest, Gradient Boosting, Bagging, and AdaBoost are selected for their ability to capture different aspects of the data and produce robust predictions. The proposed system architecture is presented, illustrating the flow of data and components. A model training and evaluation pipeline is developed, enabling the integration of ensemble learning and numerical simulation modules. The system combines the predictions generated by the ensemble models with the simulation results to produce more accurate and reliable effort estimates. The experimental setup involves a comprehensive evaluation of the proposed system. A real-world dataset comprising historical project data is utilized, and various performance metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), are employed to assess the effectiveness of the system. The results and analysis demonstrate that the ensemble learning-based effort estimation system outperforms traditional techniques, showcasing its potential to enhance project planning and resource allocation.

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Section: Related Workmentioning

confidence: 99%