Exploiting statistical correlations for proactive prediction of program behaviors

This paper presents a finding and a technique on program behavior prediction. The finding is that surprisingly strong statistical correlations exist among the behaviors of different program components (e.g., loops) and among different types of programlevel behaviors (e.g., loop trip-counts versus data values). Furthermore, the correlations can be beneficially exploited: They help resolve the proactivity-adaptivity dilemma faced by existing program behavior predictions, making it possible to gain the strengths of both approaches—the large scope and earliness of offline-profiling–based predictions, and the cross-input adaptivity of runtime sampling-based predictions. The main technique contributed by this paper centers on a new concept, seminal behaviors. Enlightened by the existence of strong correlations among program behaviors, we propose a regressionbased framework to automatically identify a small set of behaviors that can lead to accurate prediction of other behaviors in ...

• Type: Conference
• Year: 2010
• Where: CGO
• Authors: Yunlian Jiang, Eddy Z. Zhang, Kai Tian, Feng Mao, Malcom Gethers, Xipeng Shen, Yaoqing Gao
• Keywords: CGO 2010, Program Behavior, Program Behavior Prediction, Seminal Behaviors, Software Engineering