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 disruptions in its operations during and after change. Several prior techniques have solved this problem with various tradeoffs. All of them, however, assume the avail-ability of detailed component dependency models. This paper 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technique that solves this problem in settings where such models are either not available, difficult to build, or outdated due to the evo-lution of the software. Our approach first mines the ex-ecution history of a software system to infer a stochastic component dependency model, representing the proba-bilistic sequence of interactions among the system’s com-ponents. 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 eval-uated this research on a multi-user real world software system and under varying conditions.