C.3. Software Engineering
Saba Beiranvand; Mohammad Ali Zare Chahooki
Abstract
Software Cost Estimation (SCE) is one of the most widely used and effective activities in project management. In machine learning methods, some features have adverse effects on accuracy. Thus, preprocessing methods based on reducing non-effective features can improve accuracy in these methods. In clustering ...
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Software Cost Estimation (SCE) is one of the most widely used and effective activities in project management. In machine learning methods, some features have adverse effects on accuracy. Thus, preprocessing methods based on reducing non-effective features can improve accuracy in these methods. In clustering techniques, samples are categorized into different clusters according to their semantic similarity. Accordingly, in the proposed study, to improve SCE accuracy, first samples are clustered based on original features. Then, a feature selection (FS) technique is separately done for each cluster. The proposed FS method is based on a combination of filter and wrapper FS methods. The proposed method uses both filter and wrapper advantages in selecting effective features of each cluster, with less computational complexity and more accuracy. Furthermore, as the assessment criteria have significant impacts on wrapper methods, a fused criterion has also been used. The proposed method was applied to Desharnais, COCOMO81, COCONASA93, Kemerer, and Albrecht datasets, and the obtained Mean Magnitude of Relative Error (MMRE) for these datasets were 0.2173, 0.6489, 0.3129, 0.4898 and 0.4245, respectively. These results were compared with previous studies and showed improvement in the error rate of SCE.
C. Software/Software Engineering
S. Beiranvand; M.A. Z.Chahooki
Abstract
Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE ...
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Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before software project contract. Due to the uncertain nature of development estimates and in order to increase the accuracy, researchers recently have focused on machine learning techniques. Choosing the most effective features to achieve higher accuracy in machine learning is crucial. In this paper, for narrowing the semantic gap in SDEE, a hierarchical method of filter and wrapper Feature Selection (FS) techniques and a fused measurement criteria are developed in a two-phase approach. In the first phase, two stage filter FS methods provide start sets for wrapper FS techniques. In the second phase, a fused criterion is proposed for measuring accuracy in wrapper FS techniques. Experimental results show the validity and efficiency of the proposed approach for SDEE over a variety of standard datasets.
