Statistical Debugging for Real-World Performance Problems

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Statistical Debugging for Real-World Performance ProblemsLinhai SongShan Lu University of Wisconsin Madison{songlh, and implementation defects that lead to inefficientcomputation widely exist in software. These defects are dif-ficult to avoid and discover. They lead to severe performancedegradation and energy waste during production runs, andare becoming increasingly critical with the meager increaseof single-core hardware Performance and the increasing con-cerns about energy constraints. Effective tools that diagnoseperformance Problems and point out the inefficiency rootcause are sorely state of the art of Performance diagnosis is pre-liminary. Profiling can identify the functions that consumethe most computation resources, but can neither identifythe ones that waste the most resources nor explain detectors can identify specific type of in-efficient computation, but are not suited for diagnosing gen-eral Performance Problems .}

for real-world performance problems, we study how users notice and report performance problems based on 65 real- world user-reported performance problems in five repre-

  Performance, World, Statistical, Real, Debugging, Statistical debugging for real world performance