Efficient regression testing of ontology-driven systems

To manage and integrate information gathered from heterogeneous databases, an ontology is often used. Like all systems, ontology-driven systems evolve over time and must be regression tested to gain confidence in the behavior of the modified system. Because rerunning all existing tests can be extremely expensive, researchers have developed regression-test-selection (RTS) techniques that select a subset of the available tests that are affected by the changes, and use this subset to test the modified system. Existing RTS techniques have been shown to be effective, but they operate on the code and are unable to handle changes that involve ontologies. To address this limitation, we developed and present in this paper a novel RTS technique that targets ontology-driven systems. Our technique creates representations of the old and new ontologies, compares them to identify entities affected by the changes, and uses this information to select the subset of tests to rerun. We also describe in this paper OntoRetest, a tool that implements our technique and that we used to empirically evaluate our approach on two biomedical ontology-driven database systems. The results of our evaluation show that our technique is both efficient and effective in selecting tests to rerun and in reducing the overall time required to perform regression testing. ?? 2012 ACM

Spatial Pictogram Enhanced Conceptual Data Models and Their Translation to Logical Data Models

The successful development of any geographic information system project needs the careful design and implementation of spatial databases via conceptual and logical data-modeling. This involves understanding the underlying spatial data model, spatial data types and operators, spatial query languages and spatial indexing techniques. Conventional entity-relationship diagrams have limitations for conceptual spatial data-modeling, since they get cluttered with numerous spatial relationships