A holistic approach to model-based testing of Web service compositions

The behavior of composed Web services depends on the results of the invoked services; unexpected behavior of one of the invoked services can threat the correct execution of an entire composition. This paper proposes an event-based approach to black-box testing of Web service compositions based on event sequence graphs, which are extended by facilities to deal not only with service behavior under regular circumstances (i.e., where cooperating services are working as expected) but also with their behavior in undesirable situations (i.e., where cooperating services are not working as expected). Furthermore, the approach can be used independently of artifacts (e.g., Business Process Execution Language) or type of composition (orchestration/choreography). A large case study, based on a commercial Web application, demonstrates the feasibility of the approach and analyzes its characteristics. Test generation and execution are supported by dedicated tools. Especially, the use of an enterprise service bus for test execution is noteworthy and differs from other approaches. The results of the case study encourage to suggest that the new approach has the power to detect faults systematically, performing properly even with complex and large compositions. Copyright © 2012 John Wiley & Sons, Ltd.

Modelling Mathematical Reasoning in Physics Education

Many findings from research as well as reports from teachers describe students' problem solving strategies as manipulation of formulas by rote. The resulting dissatisfaction with quantitative physical textbook problems seems to influence the attitude towards the role of mathematics in physics education in general. Mathematics is often seen as a tool for calculation which hinders a conceptual understanding of physical principles. However, the role of mathematics cannot be reduced to this technical aspect. Hence, instead of putting mathematics away we delve into the nature of physical science to reveal the strong conceptual relationship between mathematics and physics. Moreover, we suggest that, for both prospective teaching and further research, a focus on deeply exploring such interdependency can significantly improve the understanding of physics. To provide a suitable basis, we develop a new model which can be used for analysing different levels of mathematical reasoning within physics. It is also a guideline for shifting the attention from technical to structural mathematical skills while teaching physics. We demonstrate its applicability for analysing physical-mathematical reasoning processes with an example.

BayGO: Bayesian analysis of ontology term enrichment in microarray data

Abstract Background The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. Results BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. Conclusion The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.