Features-Level Software Effort Estimation Using Machine Learning Algorithms

Hammad, Mustafa; Alqaddoumi, Abdulla

doi:10.1109/3ict.2018.8855752

Cited by 23 publications

(19 citation statements)

References 9 publications

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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: 93%

“…Over the past years, there have been many Machine Learning (ML) approaches in the literature that have been applied to improve the software effort estimation. Many researchers [1], [2], [3], [4], [5], also proposed different methods for optimizing the parameters of three COCOMO-based models using some of the most popular Neural Networks. Neural networks are frequently used as a tool for software effort prediction because of their aptness for arbitrary accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…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: 93%

Section: Introductionmentioning

confidence: 99%

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 [35], from software features, the authors used various machine learning algorithms to build a software effort estimation model. ANNs, support-vector machines, K-star, and linear regression machine learning algorithms were appraised on a PROMISE dataset (called Usp05-tf) with actual software efforts.…”

Section: Related Workmentioning

confidence: 99%

A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation

et al. 2022

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Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.

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“…One of the main challenges faced by Developers and Project Managers in Software Engineering is the estimation of software effort, as different project development lifecycle models require a different amount of effort at each stage of the process [8]. Traditional estimates [9] require an effort to document activities, making the estimate more complex and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

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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Features-Level Software Effort Estimation Using Machine Learning Algorithms

Cited by 23 publications

References 9 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

A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation

Extreme Learning Machine Applied to Software Development Effort Estimation

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