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Efficient human resource management needs accurate assessment and representation of available competences as well as effective mapping of required competences for specific jobs and positions. In this regard, appropriate definition and identification of competence gaps express differences between acquired and required competences. Using a detailed quantification scheme together with a mathematical approach is a way to support accurate competence analytics, which can be applied in a wide variety of sectors and fields. This article describes the combined use of software technologies and mathematical and statistical methods for assessing and analyzing competences in human resource information systems. Based on a standard competence model, which is called a Professional, Innovative and Social competence tree, the proposed framework offers flexible tools to experts in real enterprise environments, either for evaluation of employees towards an optimal job assignment and vocational training or for recruitment processes. The system has been tested with real human resource data sets in the frame of the European project called ComProFITS.

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The importance of Software Cost Estimation at the early stages of the development life cycle is clearly portrayed by the utilization of several models and methods, appeared so far in the literature. The researchers' interest has been focused on two well known techniques, namely the parametric Regression Analysis and the non-parametric Estimation by Analogy. Despite the several comparison studies, there seems to be a discrepancy in choosing the best prediction technique between them. In this paper, we introduce a semi-parametric technique, called LSEbA that achieves to combine the aforementioned methods retaining the advantages of both approaches. Furthermore, the proposed method is consistent with the mixed nature of Software Cost Estimation data and takes advantage of the whole pure information of the dataset even if there is a large amount of missing values. The paper analytically illustrates the process of building such a model and presents the experimentation on three representative datasets verifying the benefits of the proposed model in terms of accuracy, bias and spread. Comparisons of LSEbA with linear regression, estimation by analogy and a combination of them, based on the average of their outcomes are made through accuracy metrics, statistical tests and a graphical tool, the Regression Error Characteristic curves.