Automation of Software Cost Estimation using Neural Network Technique

Kumar, G. Senthil; Bhatia, Pradeep Kumar

doi:10.5120/17297-7709

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…The result showed less MRE value. The pieces of research [21] studied and merged the CO-COMO & Neural Network technique into a single structure. It was shown that the COCOMO the evaluated cost is closer to the actual cost.…”

Section: Literature Reviewmentioning

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

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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: Literature Reviewmentioning

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

show abstract

“…An automated SCE applied on COCOMO data set using Feed forward BPNN tested on COCOMO NASA 2 dataset may help project manager for fast and realistic estimation of software cost for project effort and development time [1,2]. Matlab Neural Network tool box with data from multiple projects can be used to validate, train and simulate the network with observations that neural network performs [3].…”

Section: Review Of Sce Models Based On Used Techniquementioning

confidence: 99%

Empirical Assessment and Optimization of Software Cost Estimation Using Soft Computing Techniques

Kumar

Bhatia

2016

Advanced Computing and Communication Technologies

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Software Engineering especially project planning, scheduling, monitoring and control are based on accurate estimate of the cost and effort. In the initial stage of Software Development Life Cycle (SDLC), it is hard to accurately measure software effort that may lead to possibility of project failure. Here, an empirical comparison of existing software cost estimation models based on the techniques used in those models has been elaborated using statistical criteria. On the basis of findings of empirical evaluation of existing models, a Neuro-Fuzzy Software Cost Estimation model has been proposed to hold best practices found in other models and to optimize software cost estimation. Proposed model gives good result as compared to other considered software cost estimation methods for the defined parameters in overall but it is also dependent on type of project, data and technique used in implementation.

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Neural Networks‐Based Software Development Effort Estimation: A Systematic Literature Review

Boujida,

Amazal,

Idri

2024

J Software Evolu Process

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Software development effort estimation (SDEE) is a key task in managing software projects. Among the existing SDEE models, artificial neural networks (ANN) have garnered considerable attention from the software engineering community because of their ability to learn from previous data and yield acceptable estimates. However, to the best of the authors' knowledge, no systematic literature review (SLR) has been carried out with focus on the use of ANNs in SDEE. This work aims to analyze ANN‐based SDEE studies from five view‐points: estimation accuracy, accuracy comparison, estimation context, impact of combining ANN‐based SDEE models with other techniques, and ANNs parameters. To find relevant ANN‐based SDEE studies, we carried out an automated search using four electronic databases. The quality of the relevant papers was assessed to determine the set of papers to include in our review. We identified 65 papers published in the period 1993–2023 with acceptable quality score. The results of our systematic review revealed that ANN‐based SDEE models perform better than 11 machine learning (ML) and non‐ML SDEE models. Further, the estimation accuracy is improved when neural networks are used in combination with other techniques such as fuzzy clustering techniques. This study found that the use of ANN models in SDEE is promising to get accurate estimates. However, the application of ANN models in industry is still limited. Therefore, it is recommended that practitioners cooperate with researchers to encourage and facilitate the application of ANN models in industry.

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Automation of Software Cost Estimation using Neural Network Technique

Cited by 8 publications

References 21 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

Empirical Assessment and Optimization of Software Cost Estimation Using Soft Computing Techniques

Neural Networks‐Based Software Development Effort Estimation: A Systematic Literature Review

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