Cost estimation and effort allocation are the key challenges for successful project planning and management in software development. Therefore, both industry and the research community have been working on various models and techniques to accurately predict the cost of projects. Recently, researchers have started debating whether the prediction performance depends on the structure of data rather than the models used. In this article, we focus on a new aspect of data homogeneity, ''cross-versus within-application domain'', and investigate what kind of training data should be used for software cost estimation in the embedded systems domain. In addition, we try to find out the effect of training dataset size on the prediction performance. Based on our empirical results, we conclude that it is better to use cross-domain data for embedded software cost estimation and the optimum training data size depends on the method used.

Software cost/effort estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Until today, software cost estimation has been treated as a regression problem. However, in order to prevent overestimates and underestimates, it is more practical to predict the interval of estimations instead of the exact values. In this paper, we propose an approach that converts cost estimation into a classification problem and that classifies new software projects in one of the effort classes, each of which corresponds to an effort interval. Our approach integrates cluster analysis with classification methods. Cluster analysis is used to determine effort intervals while different classification algorithms are used to find corresponding effort classes. The proposed approach is applied to seven public datasets. Our experimental results show that the hit rate obtained for effort estimation are around 90-100%, which is much higher than that obtained by related studies. Furthermore, in terms of point estimation, our results are comparable to those in the literature although a simple mean/median is used for estimation. Finally, the dynamic generation of effort intervals is the most distinctive part of our study, and it results in time and effort gain for project managers through the removal of human intervention.

Bilgi günümüzde hızla gelişiyor ve değişen ortamda önemi giderek artıyor. Her meslek gibi eğitim de reforma ihtiyaç duyan bir alandır. Günümüzün gelişen dünyasında matematik, birey, toplum, bilim ve teknoloji için elzem olan bir disiplindir. Dijital çağda uygarlıkların gelişimi için matematik öğretimi büyük önem taşıyor. Günümüzde matematik, çok çeşitli uygulamaları nedeniyle tüm disiplinlerin gerekli bir bileşenidir. Toplum içinde yaşayanlar için okulda aldıkları matematik eğitimi, edinecekleri genel matematik eğitiminin çok önemli bir bileşenidir (Baki, 2006).Matematiği günlük yaşamda kullanabilmenin ve kavrayabilmenin önemi giderek artmaktadır. Gelecek, hızla gelişen çevremizde matematiği anlayan ve uygulayan bireyler tarafından şekillendirilmekte ve inşa edilmektedir. Semboller ve formlar üzerine kurulu evrensel bir dil matematiktir. Bu dili kullanarak tahminlerde bulunmak, bilgi sağlamak ve problem çözmek matematiğin bir parçasıdır (MEB, 2006). Askar'a (1986) göre, bağımsız düşünce ve yaratıcılık gibi üst düzey davranışları geliştirebilecek bir konu olan matematiği öğrenmek kaçınılmazdır. Bu nedenle, çağdaş medeniyetler seviyesine ulaşmak için matematiğe hakim olmak gereklidir. Öğrencilere matematik öğretmek, onların analitik ve yaratıcı düşünme becerilerinin gelişimi için çok önemlidir (Altun, 2003). Olkun ve Toluk (2003), fikirlerin ve süreçlerin ve bunlar arasındaki bağlantıların anlaşılması olarak tanımlanan ve bilginin hafızaya

Software cost estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Software cost estimation, up to now, has been treated as a regression problem. However, in order to prevent over/under estimates, it is more practical to predict the interval of estimations instead of the exact values. In this paper, we propose an approach that converts cost estimation into a classification problem and classifies new software projects in one of the effort classes each corresponding to an effort interval. Our approach integrates cluster analysis with classification methods. Cluster analysis is used to determine effort intervals while different classification algorithms are used to find the corresponding effort classes. The proposed approach is applied to seven public data sets. Our experimental results show that hit rates obtained for effort estimation are around 90%-100%s. For point estimation, the results are also comparable to those in the literature.