Comparison of machine learning methods for software project effort estimation

Yurdakurban, Vehbi; Erdoğan, Nadia

doi:10.1109/siu.2018.8404495

Cited by 4 publications

(2 citation statements)

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“…Contrary to these new approaches, which are based on ANN architec-ture and require a smaller number of iterations, traditional learning algorithms are based on numerical methods that require a huge number of iterations. In accordance with that, most popular learning methods are based on Gradient Descent (GA) [5], [6], [7], [8], [9], [10] optimization. This value is important for real-time applications and security in highrisk systems, which are expected to quickly learn and adapt to their environments, and for which fast learning methods are in high demand.…”

Section: Introductionmentioning

confidence: 92%

“…Neural networks are frequently used as a tool for software effort prediction because of their aptness for arbitrary accuracy. There is an enormous area of real-world applications [3], [4], [5], [6], [7], [8], for which artificial Neural Networks (ANN) have proved very efficient due to their learning capability. Most of ANNs architecture learning refers to modifications of the values of neuron weights, which modulate signal transmission between interconnected neurons.…”

Section: Introductionmentioning

confidence: 99%

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A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

Ranković

Ivanović

et al. 2021

IEEE Access

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In this paper, two different architectures of Artificial Neural Networks (ANN) are proposed as an efficient tool for predicting and estimating software effort. Artificial Neural Networks, as a branch of machine learning, are used in estimation because they tend towards fast learning and giving better and more accurate results. The search/optimization embraced here is motivated by the Taguchi method based on Orthogonal Arrays (an extraordinary set of Latin Squares), which demonstrated to be an effective apparatus in a robust design. This paper aims to minimize the magnitude relative error (MRE) in effort estimation by using Taguchi's Orthogonal Arrays, as well as to find the simplest possible architecture of an artificial Neural Network for optimized learning. A descending gradient (GA) criterion has also been introduced to know when to stop performing iterations. Given the importance of estimating software projects, our work aims to cover as many different values of actual efficiency of a wide range of projects as possible by division into clusters and a certain coding method, in addition to the mentioned tools. In this way, the risk of error estimation can be reduced, to increase the rate of completed software projects.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

Ranković

Ivanović

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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Applying Soft Computing Techniques for Software Project Effort Estimation Modelling

Sharma

Vijayvargiya

2020

Lecture Notes in Electrical Engineering

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The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage.More recently attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Soft computing is a consortium of methodologies centering in fuzzy logic, artificial neural networks, and evolutionary computation. It is important, to mention here, that these methodologies are complementary and synergistic, rather than competitive. They provide in one form or another flexible information processing capability for handling real life ambiguous situations. These methodologies are currently used for reliable and accurate estimate of software development effort, which has always been a challenge for both the software industry and academia. The aim of this study is to analyze soft computing techniques in the existing models and to provide in depth review of software and project estimation techniques existing in industry and literature based on the different test datasets along with their strength and weaknesses.

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Extreme Learning Machine Applied to Software Development Effort Estimation

2021

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The project management process has been used in the area of Software Engineering to support project managers to keep projects under control. One of the essential processes in Software Engineering is to conduct an accurate and reliable estimation of the required effort to complete the project. This article's objectives are: i) to identify the variables that influence the estimation based on the correlation, and ii) to apply the Extreme Learning Machine -ELM model for effort estimation and compare it with the literature models. Thus, it was investigated which technique has better effort prediction accuracy. The models were compared with each other based on predictive precision in the criterion of absolute mean residue (MAR) and statistical tests. The main findings in this study were: i) important variables for effort estimation and; ii) the results indicated that the ELM model presents the best results compared to the models in the literature for estimating software design effort. In this way, the use of Machine Learning techniques in the effort estimation process can increase the chances of success in the accuracy of the time estimates and the project's costs.

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Comparison of machine learning methods for software project effort estimation

Cited by 4 publications

References 10 publications

A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

Applying Soft Computing Techniques for Software Project Effort Estimation Modelling

Extreme Learning Machine Applied to Software Development Effort Estimation

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