Genetic Algorithm and Support Vector Regression for Software Effort Estimation

Abstract:

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For software developers, accurately forecasting software effort is very important. In the field of software engineering, it is also a very challenging topic. Miscalculated software effort in the early phase might cause a serious consequence. It not only effects the schedule, but also increases the cost price. It might cause a huge deficit. Because all of the different software development team has it is own way to calculate the software effort, the factors affecting project development are also varies. In order to solve these problems, this paper proposes a model which combines genetic algorithm (GA) with support vector machines (SVM). We can find the best parameter of SVM regression by the proposed model, and make more accurate prediction. During the research, we test and verify our model by using the historical data in COCOMO. We will show the results by prediction level (PRED) and mean magnitude of relative error (MMRE).

Info:

Periodical:

Advanced Materials Research (Volumes 282-283)

Edited by:

Helen Zhang and David Jin

Pages:

748-752

DOI:

10.4028/www.scientific.net/AMR.282-283.748

Citation:

J. C. Lin and C. T. Chang, "Genetic Algorithm and Support Vector Regression for Software Effort Estimation", Advanced Materials Research, Vols. 282-283, pp. 748-752, 2011

Online since:

July 2011

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Price:

$35.00

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