BACKGROUND: Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge. RESULTS: We achieve our goal through the definition of a domain-specific language (DSL), Kendrick, that relies on a very general mathematical definition of epidemiological concerns as stochastic automata that are combined using tensor-algebra operators. A very large class of epidemiological concerns, including multi-species, spatial concerns, control policies, sex or age structures, are supported and can be defined independently of each other and combined into models to be simulated by different methods. Implementing models does not require sophisticated programming skills any more. The various concerns involved within a model can be changed independently of the others as well as reused within other models. They are not plagued by low-level implementation details. CONCLUSIONS: Kendrick is one of the few DSLs for epidemiological modelling that does not burden its users with implementation details or required sophisticated programming skills. It is also currently the only language for epidemiology modelling that supports modularity through clear separation of concerns hence fostering reproducibility and reuse of models and simulations. Future work includes extending Kendrick to support non-compartmental models and improving its interoperability with existing complementary tools.

Following publication of the original article [1], the author noticed that the following lines were missing from the published article. The original article has been corrected.

The reverse engineering of behavioral models consists in extracting high-level models that help understand the behavior of existing software systems. In the context of reverse engineering of sequence diagrams, most approaches strongly depend on the static analysis and instrumentation of the source code to produce correct diagrams that take into account control flow structures such as alternative blocks ("if"s) and repeated blocks ("loop"s). This approach is not possible with systems for which no source code is available anymore (e.g. some legacy systems). In this paper, we propose an approach for the reverse engineering of sequence diagrams from the analysis of execution traces produced dynamically by an object-oriented application. Our approach is fully based on dynamic analysis and reuses the k-tail merging algorithm to produce a Labeled Transition System (LTS) that merges the collected traces. This LTS is then translated into a sequence diagram which contains alternatives and loops. A prototype of this approach has been tested with a real world application that has been developed independently from the present work. Our results show that this approach can produce sequence diagrams in reasonable time and suggest that these diagrams are helpful in understanding the behavior of the underlying application.

We propose that design patterns be systematically analyzed and reformulated to exhibit a reasoning binding solutions to precisely stated problems, given a set of mechanisms. We show that these mechanisms can be described as program transformations and how an assistant tool could systematically explore the set of potential solutions induced by these transformations. This will both help the selection of the appropriate patterns and their instantiation in the context of the application under development.