The paper tackles the problem of notational heterogeneity in business process modeling. Heterogeneity is overcome with an approach that induces semantic homogeneity independent of notation, driven by commonalities and recurring semantics in various control flow-oriented modeling languages, with the goal of enabling process simulation on a generic level. Thus, hybrid process models (for end-to-end or decomposed processes) having different parts or subprocesses modeled with different languages become simulate-able, making it possible to derive quantitative measures (lead time, costs, or resource capacity) across notational heterogeneity. The result also contributes to a better understanding of the process structure, as it helps with identifying interface problems and process execution requirements, and can support a multitude of areas that benefit from step by step process simulation (e.g. process-oriented requirement analysis, user interface design, generation of business-related test cases, compilation of handbooks and training material derived from processes). A use case is presented in the context of the ComVantage EU research project, where notational heterogeneity is induced by: (a) the specificity and hybrid character of a process-centric modeling method designed for the project application domain, and (b) the collaborative nature of the modeling effort, with different modelers working with different notations for different layers of abstraction in a shared on-line tool and model repository.

Statistical data processing makes use of data matrices and tables as primary structures for data representation. Embedding these structures into processing-relevant context information gives rise to enhanced data structures linking data and metadata. The paper describes a framework for statistical data processing utilising metadata computationally.

Defect content estimation techniques (DCETs), based on defect data from inspection, estimate the total number of defects in a document to evaluate the development process. For inspections that yield few data points DCETs reportedly underestimate the number of defects. If there is a second inspection cycle, the additional defect data is expected to increase estimation accuracy. In this paper we consider 3 scenarios to combine data sets from the inspection-reinspection process. We evaluate these approaches with data from an experiment in a university environment where 31 teams inspected and reinspected a software requirements document. Main findings of the experiment were that reinspection data improved estimation accuracy. With the best combination approach all examined estimators yielded on average estimates within 20% around the true value, all estimates stayed within 40% around the true value.