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Shahpar, Zahra, Khatibi, Vahid, Tanavar, Asma, Sarikhani, Rahil. (1395). Improvement of effort estimation accuracy in software projects using a feature selection approach. , 2(4), 31-38.
Zahra Shahpar; Vahid Khatibi; Asma Tanavar; Rahil Sarikhani. "Improvement of effort estimation accuracy in software projects using a feature selection approach". , 2, 4, 1395, 31-38.
Shahpar, Zahra, Khatibi, Vahid, Tanavar, Asma, Sarikhani, Rahil. (1395). 'Improvement of effort estimation accuracy in software projects using a feature selection approach', , 2(4), pp. 31-38.
Shahpar, Zahra, Khatibi, Vahid, Tanavar, Asma, Sarikhani, Rahil. Improvement of effort estimation accuracy in software projects using a feature selection approach. , 1395; 2(4): 31-38.

Improvement of effort estimation accuracy in software projects using a feature selection approach

Journal of Advances in Computer Engineering and Technology
مقاله 4، دوره 2، شماره 4 - شماره پیاپی 8، بهمن 2016، صفحه 31-38  XML اصل مقاله (412.78 K)
نوع مقاله: Original Research Paper
نویسندگان
Zahra Shahpar email 1؛ Vahid Khatibi2؛ Asma Tanavar3؛ Rahil Sarikhani4
1Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman,Iran.
2Faculty Member of Islamic Azad University, Kerman Branch, Kerman,Iran.
3Department of Computer, Kerman Branch, Islamic Azad University
4Department of Computer, Kerman Branch, Islamic Azad University, Iran
چکیده
In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy.
کلیدواژه‌ها
dimensionality reduction؛ Feature Selection؛ Genetic algorithm؛ software effort estimation
مراجع
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