Juan José Cuadrado

Segmented parametric software estimation models: using the EM algorithm with the ISBSG 8 database

Parametric software estimation mod- els rely on the availability of historical project databases from which estimation models are de- rived. In the case of large project databases with data coming from heterogeneous sources, a sin- gle mathematical model cannot properly capture the diverse nature of the projects under consider- ation. In this paper, a clustering algorithm have been used as a tool to produce segmented models. A concrete case study using a modified EM algo- rithm is reported. In cases in which the cluster- ing algorithm produces an statistical characteri- zation of each of the resulting clusters, as happens with the EM algorithm, such representations can be used as similarity metrics to derive analogical estimates. This can be put in contrast with de- riving partial parametric models for each cluster. The paper also provides a comparison of quality of adjustment of both approaches.

Segmentation of software engineering datasets using the m5 algorithm

This paper reports an empirical study that uses clustering techniques to derive segmented models from software engineering repositories, focusing on the improvement of the accuracy of estimates. In particular, we used two datasets obtained from the International Software Benchmarking Standards Group (ISBSG) repository and created clusters using the M5 algorithm. Each cluster is associated with a linear model. We then compare the accuracy of the estimates so generated with the classical multivariate linear regression and least median squares. Results show that there is an improvement in the accuracy of the results when using clustering. Furthermore, these techniques can help us to understand the datasets better; such techniques provide some advantages to project managers while keeping the estimation process within reasonable complexity.

Information economy philosophy in universal education. The Open Educational Resources (OER): Technical, socioeconomics and legal aspects

According to Dr. B.R. Ambedkarpsilas definition by Deshpande, P.M. (1995), Open Educational Resources (OER) are based on the philosophical view of knowledge as a collective, social product. In consequence, it is also desirable to make it a social property. Terry Foote, one of the Wikipedia projectpsilas chairperson emphasize this: ldquoImagine a world in which every single person is given free access to the sum of all human knowledgeldquo . The importance of open educational resources (OERs) has been widely documented and demonstrated and a high magnitude impact is to be expected for OERs in the near future. This paper presents on overview of OERs and its current usage. Then, the paper goes into detailed some related aspects. Which is the impact, in socio-economic terms, of OER, especially for the less developed? Which legal aspects influence the diffusion and use of OER? And, which are the technical resources needed for them?.