Feature Selection Technique for Effective Software Effort Estimation Using Multi-Layer Perceptrons

Goyal, Somya; Bhatia, Pradeep Kumar

doi:10.1007/978-3-030-30577-2_15

Cited by 25 publications

(3 citation statements)

References 9 publications

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“…Goyal and Bhatia 31 compared the estimation accuracy of two prediction models: one without feature selection and one with feature selection. The authors used feature selection based on the neighborhood component analysis to extract the best features from the Desharnais dataset.…”

Section: Related Workmentioning

confidence: 99%

“…In other works such as in Shahpar et al 22 and Zakrani et al, 26 authors used the China dataset for the experiment and extracted 13 out of 15 features, almost all features except two. In previous studies, 23,27,31,32 the authors used neighborhood‐based RReliefF, LNI, and neighborhood component analysis feature selection techniques which calculate the variance by adapting the neighborhood boundaries based on the average and standard deviation of distances from target instance to all other, which increases the overall complexity of the models. Previous studies, 23,32,34,38 used ensemble learning for estimation; ensembling is expensive in both time and space.…”

Section: Related Workmentioning

confidence: 99%

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Towards effective feature selection in estimating software effort using machine learning

Jadhav

Shandilya

2023

J Software Evolu Process

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Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards effective feature selection in estimating software effort using machine learning

Jadhav

Shandilya

2023

J Software Evolu Process

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“…Few recent research studies have also focused on applying the hybrid approach in the SPD process. The joint approach of nature-inspired algorithm and ML is adopted by authors in [23][24][25] to compute the estimates of effort in project development. The work of Singh et al [26] evaluated different ML techniques in the software effort estimation.…”

Section: B Non-algorithmic Approachesmentioning

confidence: 99%

Effective ANN Model based on Neuro-Evolution Mechanism for Realistic Software Estimates in the Early Phase of Software Development

N¹,

Suresh²

2022

IJACSA

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There is no doubt that the software industry is one of the fastest-growing sectors on the planet today. As the cost of the entire development process continues to rise, an effective mechanism is needed to estimate the required development cost to control better the cost overrun problem and make the final software product more competitive. However, in the early stages of planning, the project managers have difficulty estimating the realistic value of the effort and cost required to execute development activities. Software evaluation prior to development can minimize risk and upsurge project success rates. Many techniques have been suggested and employed for cost estimation. However, computations based on several of these techniques show that the estimation of development effort and cost vary, which may cause problems for software industries in allocating overall resources costs. The proposed research study proposes the artificial neural network (ANN) based Neural-Evolution technique to provide more realistic software estimates in the early stages of development. The proposed model uses the advantages of the topology augmentation using an evolutionary algorithm to automate and achieve optimality in ANN construction and training.Based on the results and performance analysis, it is observed that software effort prediction using the proposed approach is more accurate and better than other existing approaches.

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Software Quality Prediction Using Machine Learning Techniques

Goyal

Bhatia

2020

Advances in Intelligent Systems and Computing

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Feature Selection Technique for Effective Software Effort Estimation Using Multi-Layer Perceptrons

Cited by 25 publications

References 9 publications

Towards effective feature selection in estimating software effort using machine learning

Towards effective feature selection in estimating software effort using machine learning

Effective ANN Model based on Neuro-Evolution Mechanism for Realistic Software Estimates in the Early Phase of Software Development

Software Quality Prediction Using Machine Learning Techniques

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