Speaker: Kyle Canavera
Title: Mining the Execution History of a Software System to Infer the Best Time for its Adaptation.
Date/Time: Thursday, 11/8/2012 @ 10:30am
Location: 4201, Engineering Building
Abstract:
An important challenge in dynamic adaptation of a software system is to prevent inconsistencies (failures) and disruptions in its operations during and after change. Several prior techniques have solved this problem with various tradeoffs. All of them, however, assume the availability of detailed component dependency models. This talk presents a complementary technique that solves this problem in settings where such models are either not available, difficult to build, or outdated due to the evolution of the software. Our approach first mines the execution history of a software system to infer a stochastic component dependency model, representing the probabilistic sequence of interactions among the system’s components. We then demonstrate how this model could be used at runtime to infer the “best time” for adaptation of the system’s components. We have thoroughly evaluated this research on a multi-user real world software system and under varying conditions.
Bio:
Kyle Canavera is a student in the Ph.D. program in the Department of Computer Science at George Mason University. Kyle's general research interests are in the fields of software engineering, data mining, and intellectual property law. Kyle received his BS degree in Computer Science from Xavier University in Cincinnati, Ohio.