Data Mining and Machine Learning for System Model Discovery and Applications

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Current day software infrastructures, cyber-physical systems, and Internet of Things (IoT) have become extremely complex for engineers to understand and debug. The risks are higher if the system is safety-critical. The increase in complexity of software systems presents a challenging problem of providing assurances of their reliable operation. Moreover, the lack of good quality behavioral description (both at macro and micro levels of abstraction) of these systems is one of the major problems. Due to their inherent complexity, and rapid evolution of software systems to meet the demands of clients, software applications and libraries are often released without documented specifications. Furthermore, lack of specifications negatively impacts the maintainability and reliability of systems. My research program, `Data Mining and Machine Learning for System Model Discovery and Applications', focuses on developing novel, faster, intelligent algorithms, and frameworks, and on seeing their broader impact through practical applications. The primary goal of my research program is to develop algorithms, models, and tools that infer the model/behavior of complex software systems considering them as a 'black box'. I propose to concentrate on model inference of complex software systems (both ubiquitous and large scale infrastructures). Specifically, I aim to infer system behavior, visualize it, and use it to make complex systems understandable, robust and reliable. Models developed as a part of this research program will not only infer complex system behavior model, but also incorporate the results into an experimentation framework for system safety and security. Consequently, users will benefit from the latest results at runtime enabling safe, secure, and resilient cyber-physical systems. The outcome of this work will be a set of open source software tools that will help engineers to build and analyze systems quicker and with fewer mistakes. This work will be relevant to both industry and academics. It will solve everyday problems faced by software engineers as well as extend our understanding of how to reason the complex behavior of software infrastructures. Further, this research will provide important and invaluable training to both graduate and undergraduate students. It will produce highly-qualified personnel with `data mining and machine learning skill-set' that is valuable and actively sought after by all the major software, hardware, and consulting companies in Canada. Lastly, this research program will develop strong partnerships between my group with industries and academia.