Exploring program phases for statistical bug localization

Statistical bug isolation techniques attempt to capture a correlation of various program features (like predicates and profiled paths) for debugging. These techniques collect profile data for multiple executions, both with successful and faulty runs, and propose using various statistical tests to capture this correlation. In this paper, we explore the utility of program phases, a concept which is primarily used by computer architects to speed up architectural simulations, for statistical bug isolation. Program phases represent sets of execution intervals in a program's execution where the rates of architectural statistics like branch mispredictions, CPU/Memory usage and cache misses remain almost the same. We found multiple scenarios where coupling program phases with predicates achieves higher accuracy to bug localization than when predicates are used alone. We demonstrate the use of program phases for bug isolation by presenting experimental results and concrete case studies on medium-size programs, showing an improved ranking of the program points that are critical to debugging over when program phases are not used.