EAGER: CCF: SHF: Mining the Execution History of a Software System to Infer the Safe Time for its Adaptation

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As software engineers have developed new techniques to address the complexity associated with the construction of modern-day software systems, an equally pressing need has risen for mechanisms that automate and simplify the management of those systems after they are deployed, i.e., during runtime. This has called for the development of (self-)adaptive software systems, which are capable of modifying their behavior at runtime to achieve certain functional or quality of service objectives. The proposed research aims to develop an alternative approach to engineering adaptive software that uses a data mining approach to automatically derive models expressing probabilistic dependencies among the components of a system. These types of models are then used to ensure changes in the running software do not create inconsistencies that jeopardize the system?s functionality. The hypothesis guiding this research is that by monitoring a software system?s execution history (e.g., message exchange, method invocation) for a sufficiently long period of time, it is possible to infer a relatively accurate model of interactions and dependencies among the system?s components. The proposed approach will be realized via a suite of integrated tools. The research will be evaluated in both controlled laboratory setting, as well as several real-world applications that are representative of the kinds of systems that could benefit from this research.