Mining the Execution History of a Software System to Infer the Best Time for Its Adaptation

An important challenge in dynamic adaptation of a soft-ware system is to prevent inconsistencies (failures) and dis-ruptions in its operations during and after change. Several prior techniques have solved this problem with various trade-offs. All of them, however, assume the availability of de-tailed component dependency models. This paper presents a complementary technique that solves this problem in set-tings where such models are either not available, difficult to build, or outdated due to the evolution of the software.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 sys-tem to infer a stochastic component dependency model, rep-resenting 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