Article: Risk integrated effort estimation of software projects: a comparative analysis of machine learning techniques Journal: International Journal of System of Systems Engineering (IJSSE) 2025 Vol.15 No.2 pp.166 - 195 Abstract: Accurate software project effort estimation and risk management are the pillars of delivering an on-time, within budget and quality project. In our earlier research, a formula for computing risk integrated effort estimate by adding weighted cost of risk management for each project cost factor to the cost of initial effort estimate of the project has been proposed. In this research, neural network techniques: multilayer perceptron (MLP), general regression neural network (GRNN), cascade correlation neural network (CCNN) and radial bias function neural network (RBFNN); support vector regression (SVR), and adaptive neuro fuzzy inference system (ANFIS) to obtain the integrated effort estimate as close as possible to the actual effort spent on the project have been applied. The techniques have been tested on two datasets: Agile and Waterfall datasets. GRNN gave the best results in terms of lowest values of accuracy measures: mean absolute error (MAE), mean magnitude of relative error (MMRE), mean balanced relative error (MBRE), and mean inverted balanced relative error (MIBRE). This research also compares performance of GRNN with evolutionary algorithms artificial bee colony (ABC), particle swarm optimisation (PSO) and global local binary particle swarm optimisation (GLBPSO), and the results for GRNN are comparable. Inderscience Publishers - linking academia, business and industry through research

Title: Risk integrated effort estimation of software projects: a comparative analysis of machine learning techniques

Authors: Prerna Singal; Prabha Sharma; A. Charan Kumari

Addresses: Department of CSE, The NorthCap University, Gurugram, 122017, India ' Department of CSE, The NorthCap University, Gurugram, 122017, India ' Department of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra, 282 005, UP, India

Abstract: Accurate software project effort estimation and risk management are the pillars of delivering an on-time, within budget and quality project. In our earlier research, a formula for computing risk integrated effort estimate by adding weighted cost of risk management for each project cost factor to the cost of initial effort estimate of the project has been proposed. In this research, neural network techniques: multilayer perceptron (MLP), general regression neural network (GRNN), cascade correlation neural network (CCNN) and radial bias function neural network (RBFNN); support vector regression (SVR), and adaptive neuro fuzzy inference system (ANFIS) to obtain the integrated effort estimate as close as possible to the actual effort spent on the project have been applied. The techniques have been tested on two datasets: Agile and Waterfall datasets. GRNN gave the best results in terms of lowest values of accuracy measures: mean absolute error (MAE), mean magnitude of relative error (MMRE), mean balanced relative error (MBRE), and mean inverted balanced relative error (MIBRE). This research also compares performance of GRNN with evolutionary algorithms artificial bee colony (ABC), particle swarm optimisation (PSO) and global local binary particle swarm optimisation (GLBPSO), and the results for GRNN are comparable.

Keywords: agile projects; risk management; neural networks; CoCoMo II; SVR; support vector regression; risk exposure.

DOI: 10.1504/IJSSE.2025.146198

International Journal of System of Systems Engineering, 2025 Vol.15 No.2, pp.166 - 195

Received: 16 Jan 2023
Accepted: 29 Jun 2023
Published online: 12 May 2025 *

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Title: Risk integrated effort estimation of software projects: a comparative analysis of machine learning techniques

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