The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Software Complexity Metric-based Defect Classification Using FARM with Preprocessing Step CFS and SMOTE A Preliminary Study
One criteria for assessing the software quality is ensuring that there is no defect in the software which is being developed. Software defect classification can be used to prevent software defects. More earlier software defects are detected in the software life cycle, it will minimize the software development costs. This study proposes a software defect classification using Fuzzy Association Rule Mining (FARM) based on complexity metrics. However, not all complexity metrics affect on software defect, therefore it requires metrics selection process using Correlation-based Feature Selection (CFS) so it can increase the classification performance. This study will conduct experiments on the NASA MDP open source dataset that is publicly accessible on the PROMISE repository. This datasets contain history log of software defects based on software complexity metric. In NASA MDP dataset the data distribution between defective and not defective modules are not balanced. It is called class imbalanced problem. Class imbalance problem can affect on classification performance. It needs a technique to solve this problem using oversampling method. Synthetic Minority Oversampling Technique (SMOTE) is used in this study as oversampling method. With the advantages possessed by FARM in learning on dataset which has quantitative data attribute and combined with the software complexity metrics selection process using CFS and oversampling using SMOTE, this method is expected has a better performance than the previous methods.
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