Software engineering

Today's software systems generally use obsolete technology, are not integrated properly with other software systems, and are difficult and costly to maintain. The discipline of reverse engineering is becoming prominent as organizations try to move their systems up to more modern and maintainable technology in a cost effective manner. The Johnson Space Center (JSC) created a significant set of tools to develop and maintain FORTRAN and C code during development of the space shuttle. This tool set forms the basis for an integrated environment to reengineer existing code into modern software engineering structures which are then easier and less costly to maintain and which allow a fairly straightforward translation into other target languages. The environment will support these structures and practices even in areas where the language definition and compilers do not enforce good software engineering. The knowledge and data captured using the reverse engineering tools is passed to standard forward engineering tools to redesign or perform major upgrades to software systems in a much more cost effective manner than using older technologies. The latest release of the environment was in Feb. 1992

A reverse predictive model towards design automation of microfluidic droplet generators

This work has been presented in the 10th IWBDA workshop.Droplet-based microfluidic devices in comparison to test tubes can reduce reaction volumes 10^9 times and more due to the encapsulation of reactions in micro-scale droplets [4]. This volume reduction, alongside higher accuracy, higher sensitivity and faster reaction time made droplet microfluidics a superior platform particularly in biology, biomedical, and chemical engineering. However, a high barrier of entry prevents most of life science laboratories to exploit the advantages of microfluidics. There are two main obstacles to the widespread adoption of microfluidics, high fabrication costs, and lack of design automation tools. Recently, low-cost fabrication methods have reduced the cost of fabrication significantly [7]. Still, even with a low-cost fabrication method, due to lack of automation tools, life science research groups are still reliant on a microfluidic expert to develop any new microfluidic device [3, 5]. In this work, we report a framework to develop reverse predictive models that can accurately automate the design process of microfluidic droplet generators. This model takes prescribed performance metrics of droplet generators as the input and provides the geometry of the microfluidic device and the fluid and flow settings that result in the desired performance. We hope this automation tool makes droplet-based microfluidics more accessible, by reducing the time, cost, and knowledge needed for developing a microfluidic droplet generator that meets certain performance requirement

Learning Concise Models from Long Execution Traces

Abstract models of system-level behaviour have applications in design exploration, analysis, testing and verification. We describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest. Our algorithm leverages modern program synthesis techniques to generate predicates on automaton edges, succinctly describing system behaviour. It employs trace segmentation to tackle complexity for long traces. We learn concise models capturing transaction-level, system-wide behaviour--experimentally demonstrating the approach using traces from a variety of sources, including the x86 QEMU virtual platform and the Real-Time Linux kernel