Model Based Analysis and Test Generation for Flight Software

We describe a framework for model-based analysis and test case generation in the context of a heterogeneous model-based development paradigm that uses and combines Math- Works and UML 2.0 models and the associated code generation tools. This paradigm poses novel challenges to analysis and test case generation that, to the best of our knowledge, have not been addressed before. The framework is based on a common intermediate representation for different modeling formalisms and leverages and extends model checking and symbolic execution tools for model analysis and test case generation, respectively. We discuss the application of our framework to software models for a NASA flight mission

A model for the role of feedback in ocular dominance column development.

Feedback is ubiquitous in the mammalian brain, and has been shown to play a very important role in perception and cognition. However, feedback is also present in the developing brain, and as such may play an important role in brain development. We present a model to investigate the role of feedback during brain development, specifically in formation of ocular dominance columns (ODC's). Feedback is a unique feature of our model. Results of anatomical and physiological experiments show that feedback loops exist between cortical layer-4 and other neuron populations (the subplate and cortical layer-6) during ODC development. Our model puts forth the hypothesis that these neuron populations locally integrate the activity of layer-4 neurons and feed it back to same, specifically to the post-synaptic sites of geniculocortical NMDA synapses. In this way, the feedback activity directly controls the synaptic strength changes, causing the development of ODC's. Another unique feature of our model is that the ALOPEX algorithm is used change the geniculocortical synaptic strengths. Unlike the Hebb rule, ALOPEX is naturally suited to systems with feedback. Through detailed neuronal simulations, we demonstrate the biophysical feasibility of ALOPEX in the current system. Computer simulations demonstrate that our model is extremely robust. The average column width remains constant against variations in thalamic and cortical parameters. When biologically realistic parameter values are used, the model renders ODC's of an appropriate width. Further, the model successfully replicates the effects of monocular deprivation, reversed monocular deprivation, and strabismus. It also renders a consistent column width under different scales of cortical representation. From these results, we conclude that the role of feedback is to make development robust against biological variabilities.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/104524/1/9527712.pdfDescription of 9527712.pdf : Restricted to UM users only

1 Rapid Property Specification and Checking for Model-Based Formalisms

Abstract—In model-based development, verification techniques can be used to check whether an abstract model satisfies a set of properties. Ideally, implementation code generated from these models can also be verified against similar properties. However, the distance between the property specification languages and the implementation makes verifying such generated code difficult. Optimizations and renamings can blur the correspondence between the two, further increasing the difficulty of specifying verification properties on the generated code. This paper describes models that are then checked on implementation level code. These properties are translated by an extended code generator into implementation code and special annotations that are used by a software model checker. I