Experimental Evaluation of Algorithmic Effort Estimation Models using Projects Clustering
Publish Year: 1395
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JACET-2-3_003
تاریخ نمایه سازی: 18 تیر 1398
Abstract:
One of the most important aspects of software project management is the estimation of cost and time required for running information system. Therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. Software cost estimation refers to the process of development requirement prediction of software system. Various kinds of effort estimation patterns have been presented in recent years, which are focused on intelligent techniques. This study made use of clustering approach for estimating required effort in software projects. The effort estimation is carried out through SWR (StepWise Regression) and MLR (Multiple Linear Regressions) regression models as well as CART (Classification And Regression Tree) method. The performance of these methods is experimentally evaluated using real software projects. Moreover, clustering of projects is applied to the estimation process. As indicated by the results of this study, the combination of clustering method and algorithmic estimation techniques can improve the accuracy of estimates.
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Authors
Farzaneh Famoori
Department of Computer Engineering, Islamic Azad University, Kerman Branch. Kerman, Iran.
Vahid Khatibi bardsiri
Department of Computer Engineering, Islamic Azad University, Kerman Branch
Shima Javadi Moghadam
Department of Computer Engineering, Islamic Azad University, Kerman Branch, Krman, Iran.
Fakhrosadat Fanian
Department of Computer Engineering, Islamic Azad University, Kerman Branch, Kerman Iran.