Data Mining and Machine Learning for System Model Discovery and Applications

当今的软件基础设施、网络物理系统和物联网(IoT)对于工程师来说已经变得极其复杂，难以理解和调试。如果系统是安全关键型的，风险就会更高。软件系统复杂性的增加提出了一个具有挑战性的问题，即为其可靠运行提供保证。此外，缺乏对这些系统的高质量的行为描述(宏观和微观抽象级别)是主要问题之一。由于软件应用程序和库本身的复杂性，以及软件系统为满足客户需求而快速发展，软件应用程序和库通常在未记录规范的情况下发布。此外，缺乏规范对系统的可维护性和可靠性产生了负面影响。我的研究项目‘用于系统模型发现和应用的数据挖掘和机器学习’，专注于开发新的、更快的、智能的算法和框架，并通过实际应用看到它们更广泛的影响。我的研究计划的主要目标是开发算法、模型和工具，以推断复杂软件系统的模型/行为，并将其视为“黑匣子”。我建议将重点放在复杂软件系统(包括无处不在的和大规模的基础设施)的模型推理上。具体地说，我的目标是推断系统行为，将其可视化，并使用它来使复杂的系统易于理解、健壮和可靠。作为本研究计划的一部分，开发的模型不仅将推断复杂的系统行为模型，而且还将结果纳入系统安全和保障的实验框架中。因此，用户将从运行时的最新结果中受益，从而实现安全、可靠和弹性的网络物理系统。这项工作的结果将是一套开源软件工具，将帮助工程师更快地构建和分析系统，并减少错误。这项工作将对工业界和学术界都有意义。它将解决软件工程师面临的日常问题，并扩展我们对如何推理软件基础设施的复杂行为的理解。此外，这项研究将为研究生和本科生提供重要和宝贵的培训。它将培养出具有数据挖掘和机器学习技能的高素质人才，这是加拿大所有主要软件、硬件和咨询公司的宝贵和积极追捧的技能。最后，这项研究计划将在我的团队与产业界和学术界之间建立牢固的伙伴关系。

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.