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One of the most important problems of software projects is software cost estimation. Software cost estimation is the estimation of the budget required during the actualization of the software. The project manager reduces the ambuguities in the project by estimating the project cost correctly. Otherwise, serious economic problems will arise. 60% of large projects exceeded the estimated project budgets. It has been observed that a number of projects were never completed due to a 15% cost overrun. As a result of the growth and complexity of software projects, new cost estimation methods are constantly being developed. In this study, the cost of software projects is tried to * Sorumlu Yazar: sukranebren@hotmail.com Avrupa Bilim ve Teknoloji Dergisi e-ISSN: 2148-2683 416 be estimated by using machine learning algorithms. Project cost estimation has been made by testing in 27 different machine learning algorithms in the waikato environment (WEKA) for information analysis. Algorithms were applied to COCOMO81, COCOMONASA and COCOMONASA2 datasets taken from PROMISE data store with 10-fold cross validation technique and results, performance criterion correlation coefficient, error rates mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE).When the results were examined, it was found that an algorithm does not always produce the best result, but produces different results with different data sets. It has been observed that some algorithms work very well in some data sets and may give poor results with different parameters and different data sets. Thanks to this study, the information about which algorithms can be used for software cost estimation, what the estimation results might be when these algorithms are applied to COCOMO data sets and which algorithms are the best working have been reached.

Yazılım Projelerinin Maliyet Tahmini için WEKA’da Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Analizi