Predictive Approach towards Software Effort Estimation using Evolutionary Support Vector Machine

Mahboob, Tahira; Gull, Sabheen; Ehsan, Sidrish; Sikandar, Bushra

doi:10.14569/ijacsa.2017.080554

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The goal of this [16] work is to give an approach for accurately estimating the time and resources required to complete a software development project using only a subset of that organization's past project data. Two techniques, the correlation matrix, and the decision tree were utilized to determine the optimal prediction parameters.…”

Section: Related Workmentioning

confidence: 99%

Software Effort Estimation using Machine Learning Technique

Rahman¹,

Roy²,

Ali³

et al. 2023

IJACSA

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Software engineering effort estimation plays a significant role in managing project cost, quality, and time and creating software. Researchers have been paying close attention to software estimation during the past few decades, and a great amount of work has been done utilizing a variety of machinelearning techniques and algorithms. In order to better effectively evaluate predictions, this study recommends various machine learning algorithms for estimating, including k-nearest neighbor regression, support vector regression, and decision trees. These methods are now used by the software development industry for software estimating with the goal of overcoming the limitations of parametric and conventional estimation techniques and advancing projects. Our dataset, which was created by a software company called Edusoft Consulted LTD, was used to assess the effectiveness of the established method. The three commonly used performance evaluation measures, mean absolute error (MAE), mean squared error (MSE), and R square error, represent the base for these. Comparative experimental results demonstrate that decision trees perform better at predicting effort than other techniques.

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Section: Related Workmentioning

confidence: 99%

Software Effort Estimation using Machine Learning Technique

Rahman¹,

Roy²,

Ali³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…In (Mahboob et al, 2017) a solution was found to determine the effort required for a software project based on an organizations historical data for projects. The solution, based on a predictive model, is a result of research which includes two methods i.e., (a) correlation matrix and (b) decision tree.…”

Section: Related Workmentioning

confidence: 99%

Risk Classification in Global Software Development Using a Machine Learning Approach

Iftikhar

Musa

Alam

et al. 2022

Journal of Information Technology Research

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Software development through teams at different geographical locations is a trend of modern era, which is not only producing good results without costing lot of money but also productive in relation to its cost, low risk and high return. This shift of perception of working in a group rather than alone is getting stronger day by day and has become an important planning tool and part of their business strategy. In this research classification approaches like SVM and K-NN have been implemented to classify the true positive events of global software development project risk according to Time, Cost and Resource. Comparative analysis has also been performed between these two algorithms to determine the highest accuracy algorithms. Results proved that Support Vector Machine (SVM) performed very well in case of Cost Related Risk and Resource Related Risk. Whereas, KNN is found superior to SVM for Time Related Risk.

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Predictive Approach towards Software Effort Estimation using Evolutionary Support Vector Machine

Cited by 2 publications

References 18 publications

Software Effort Estimation using Machine Learning Technique

Software Effort Estimation using Machine Learning Technique

Risk Classification in Global Software Development Using a Machine Learning Approach

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