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Research

  • Feature Model for Cell-to-Cell Signalling

    Software Engineering needs to address an increasingly significant class of programs that are self-adaptive and self-healing. These programs sense changes to their environment and react by modifying configurations, libraries or program code. Furthermore, automated approaches for program repair and program transplantation change a program's source code directly to fix, optimize or add new functionality. Together, self-modification provides continual availability in the presence of change and can harden a system against intruders. While this organic nature of self-modification is a powerful paradigm, the overall dependability and security of such programs is at risk.  This project draws inspiration from nature and uses bio-inspired techniques to design testing techniques on these programs.

  • CRN represented in Matlab

    Bioinformatics tools, and programming via chemical reaction networks (CRNs), result in simulations of a natural process such as an organism's growth, the interactions between molecules as reactions execute over time.  While these types of abstractions form a powerful and growing computational paradigm, these are encoded as software programs, which simulate the natural processes, and hence they are prone to faults. This project develops foundations for software testing of natural representations. It is developing techniques for  test generation with measurable code and model coverage and designing configuration-aware testing and optimization techniques for these systems.